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Encore Episode: Generative AI and Large Language Models 05/14/2024

Encore Episode: Generative AI and Large Language Models In this week’s episode, Lois Houston and Nikita Abraham, along with Senior Instructor Himanshu Raj, take you through the extraordinary capabilities of Generative AI, a subset of deep learning that doesn’t make predictions but rather creates its own content. They also explore the workings of Large Language Models. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. --------------------------------------------------------- Episode Transcript: 00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:46 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! In our last episode, we went over the basics of deep learning. Today, we’ll look at generative AI and large language models, and discuss how they work. To help us with that, we have Himanshu Raj, Senior Instructor on AI/ML. So, let’s jump right in. Hi Himanshu, what is generative AI? 01:21 Himanshu: Generative AI refers to a type of AI that can create new content. It is a subset of deep learning, where the models are trained not to make predictions but rather to generate output on their own. Think of generative AI as an artist who looks at a lot of paintings and learns the patterns and styles present in them. Once it has learned these patterns, it can generate new paintings that resembles what it learned. 01:48 Lois: Let's take an example to understand this better. Suppose we want to train a generative AI model to draw a dog. How would we achieve this? Himanshu: You would start by giving it a lot of pictures of dogs to learn from. The AI does not know anything about what a dog looks like. But by looking at these pictures, it starts to figure out common patterns and features, like dogs often have pointy ears, narrow faces, whiskers, etc. You can then ask it to draw a new picture of a dog. The AI will use the patterns it learned to generate a picture that hopefully looks like a dog. But remember, the AI is not copying any of the pictures it has seen before but creating a new image based on the patterns it has learned. This is the basic idea behind generative AI. In practice, the process involves a lot of complex maths and computation, and there are different techniques and architectures that can be used, such as variational autoencoders (VAs) and Generative Adversarial Networks (GANs). 02:48 Nikita: Himanshu, where is generative AI used in the real world? Himanshu: Generative AI models have a wide variety of applications across numerous domains. For the image generation, generative models like GANs are used to generate realistic images. They can be used for tasks, like creating artwork, synthesizing images of human faces, or transforming sketches into photorealistic images. For text generation, large language models like GPT 3, which are generative in nature, can create human-like text. This has applications in content creation, like writing articles, generating ideas, and again, conversational AI, like chat bots, customer service agents. They are also used in programming for code generation and debugging, and much more. For music generation, generative AI models can also be used. They create new pieces of music after being trained on a specific style or collection of tunes. A famous example is OpenAI's MuseNet. 03:42 Lois: You mentioned large language models in the context of text-based generative AI. So, let’s talk a little more about it. Himanshu, what exactly are large language models? Himanshu: LLMs are a type of artificial intelligence models built to understand, generate, and process human language at a massive scale. They were primarily designed for sequence to sequence tasks such as machine translation, where an input sequence is transformed into an output sequence. LLMs can be used to translate text from one language to another. For example, an LLM could be used to translate English text into French. To do this job, LLM is trained on a massive data set of text and code which allows it to learn the patterns and relationships that exist between different languages. The LLM translates, “How are you?” from English to French, “Comment allez-vous?” It can also answer questions like, what is the capital of France? And it would answer the capital of France is Paris. And it will write an essay on a given topic. For example, write an essay on French Revolution, and it will come up with a response like with a title and introduction. 04:53 Lois: And how do LLMs actually work? Himanshu: So, LLM models are typically based on deep learning architectures such as transformers. They are also trained on vast amount of text data to learn language patterns and relationships, again, with a massive number of parameters usually in order of millions or even billions. LLMs have also the ability to comprehend and understand natural language text at a semantic level. They can grasp context, infer meaning, and identify relationships between words and phrases. 05:26 Nikita: What are the most important factors for a large language model? Himanshu: Model size and parameters are crucial aspects of large language models and other deep learning models. They significantly impact the model’s capabilities, performance, and resource requirement. So, what is model size? The model size refers to the amount of memory required to store the model's parameter and other data structures. Larger model sizes generally led to better performance as they can capture more complex patterns and representation from the data. The parameters are the numerical values of the model that change as it learns to minimize the model's error on the given task. In the context of LLMs, parameters refer to the weights and biases of the model's transformer layers. Parameters are usually measured in terms of millions or billions. For example, GPT-3, one of the largest LLMs to date, has 175 billion parameters making it extremely powerful in language understanding and generation. Tokens represent the individual units into which a piece of text is divided during the processing by the model. In natural language, tokens are usually words, subwords, or characters. Some models have a maximum token limit that they can process and longer text can may require truncation or splitting. Again, balancing model size, parameters, and token handling is crucial when working with LLMs. 06:49 Nikita: But what’s so great about LLMs? Himanshu: Large language models can understand and interpret human language more accurately and contextually. They can comprehend complex sentence structures, nuances, and word meanings, enabling them to provide more accurate and relevant responses to user queries. This model can generate human-like text that is coherent and contextually appropriate. This capability is valuable for context creation, automated writing, and generating personalized response in applications like chatbots and virtual assistants. They can perform a variety of tasks. Large language models are very versatile and adaptable to various industries. They can be customized to excel in applications such as language translation, sentiment analysis, code generation, and much more. LLMs can handle multiple languages making them valuable for cross-lingual tasks like translation, sentiment analysis, and understanding diverse global content. Large language models can be again, fine-tuned for a specific task using a minimal amount of domain data. The efficiency of LLMs usually grows with more data and parameters. 07:55 Lois: You mentioned the “sequence to sequence tasks” earlier. Can you explain the concept in simple terms for us? Himanshu: Understanding language is difficult for computers and AI systems. The reason being that words often have meanings based on context. Consider a sentence such as Jane threw the frisbee, and her dog fetched it. In this sentence, there are a few things that relate to each other. Jane is doing the throwing. The dog is doing the fetching. And it refers to the frisbee. Suppose we are looking at the word “it” in the sentence. As a human, we understand easily that “it” refers to the frisbee. But for a machine, it can be tricky. The goal in sequence problems is to find patterns, dependencies, or relationships within the data and make predictions, classification, or generate new sequences based on that understanding. 08:48 Lois: And where are sequence models mostly used? Himanshu: Some common example of sequence models includes natural language processing, which we call NLP, tasks such as machine translation, text generation sentiment analysis, language modeling involve dealing with sequences of words or characters. Speech recognition. Converting audio signals into text, involves working with sequences of phonemes or subword units to recognize spoken words. Music generation. Generating new music involves modeling musical sequences, nodes, and rhythms to create original compositions. Gesture recognition. Sequences of motion or hand gestures are used to interpret human movements for applications, such as sign language recognition or gesture-based interfaces. Time series analysis. In fields such as finance, economics, weather forecasting, and signal processing, time series data is used to predict future values, detect anomalies, and understand patterns in temporal data. 09:56 The Oracle University Learning Community is an excellent place to collaborate and learn with Oracle experts and fellow learners. Grow your skills, inspire innovation, and celebrate your successes. All your activities, from liking a post to answering questions and sharing with others, will help you earn a valuable reputation, badges, and ranks to be recognized in the community. Visit mylearn.oracle.com to get started. 10:23 Nikita: Welcome back! Himanshu, what would be the best way to solve those sequence problems you mentioned? Let’s use the same sentence, “Jane threw the frisbee, and her dog fetched it” as an example. Himanshu: The solution is transformers. It's like model has a bird's eye view of the entire sentence and can see how all the words relate to each other. This allows it to understand the sentence as a whole instead of just a series of individual words. Transformers with their self-attention mechanism can look at all the words in the sentence at the same time and understand how they relate to each other. For example, transformer can simultaneously understand the connections between Jane and dog even though they are far apart in the sentence. 11:13 Nikita: But how? Himanshu: The answer is attention, which adds context to the text. Attention would notice dog comes after frisbee, fetched comes after dog, and it comes after fetched. Transformer does not look at it in isolation. Instead, it also pays attention to all the other words in the sentence at the same time. But considering all these connections, the model can figure out that “it” likely refers to the frisbee. The most famous current models that are emerging in natural language processing tasks consist of dozens of transformers or some of their variants, for example, GPT or Bert. 11:53 Lois: I was looking at the AI Foundations course on MyLearn and came across the terms “prompt engineering” and “fine tuning.” Can you shed some light on them? Himanshu: A prompt is the input or initial text provided to the model to elicit a specific response or behavior. So, this is something which you write or ask to a language model. Now, what is prompt engineering? So prompt engineering is the process of designing and formulating specific instructions or queries to interact with a large language model effectively. In the context of large language models, such as GPT 3 or Burt, prompts are the input text or questions given to the model to generate responses or perform specific tasks. The goal of prompt engineering is to ensure that the language model understands the user's intent correctly and provide accurate and relevant responses. 12:47 Nikita: That sounds easy enough, but fine tuning seems a bit more complex. Can you explain it with an example? Himanshu: Imagine you have a versatile recipe robot named chef bot. Suppose that chef bot is designed to create delicious recipes for any dish you desire. Chef bot recognizes the prompt as a request for a pizza recipe, and it knows exactly what to do. However, if you want chef bot to be an expert in a particular type of cuisine, such as Italian dishes, you fine-tune chef bot for Italian cuisine by immersing it in a culinary crash course filled with Italian cookbooks, traditional Italian recipes, and even Italian cooking shows. During this process, chef bot becomes more specialized in creating authentic Italian recipes, and this option is called fine tuning. LLMs are general purpose models that are pre-trained on large data sets but are often fine-tuned to address specific use cases. When you combine prompt engineering and fine tuning, and you get a culinary wizard in chef bot, a recipe robot that is not only great at understanding specific dish requests but also capable of following a specific dish requests and even mastering the art of cooking in a particular culinary style. 14:08 Lois: Great! Now that we’ve spoken about all the major components, can you walk us through the life cycle of a large language model? Himanshu: The life cycle of a Large Language Model, LLM, involves several stages, from its initial pre-training to its deployment and ongoing refinement. The first of this lifecycle is pre-training. The LLM is initially pre-trained on a large corpus of text data from the internet. During pre-training, the model learns grammar, facts, reasoning abilities, and general language understanding. The model predicts the next word in a sentence given the previous words, which helps it capture relationships between words and the structure of language. The second phase is fine tuning initialization. After pre-training, the model's weights are initialized, and it's ready for task-specific fine tuning. Fine tuning can involve supervised learning on labeled data for specific tasks, such as sentiment analysis, translation, or text generation. The model is fine-tuned on specific tasks using a smaller domain-specific data set. The weights from pre-training are updated based on the new data, making the model task aware and specialized. The next phase of the LLM life cycle is prompt engineering. So this phase craft effective prompts to guide the model's behavior in generating specific responses. Different prompt formulations, instructions, or context can be used to shape the output. 15:34 Nikita: Ok… we’re with you so far. What’s next? Himanshu: The next phase is evaluation and iteration. So models are evaluated using various metrics to access their performance on specific tasks. Iterative refinement involves adjusting model parameters, prompts, and fine tuning strategies to improve results. So as a part of this step, you also do few shot and one shot inference. If needed, you further fine tune the model with a small number of examples. Basically, few shot or a single example, one shot for new tasks or scenarios. Also, you do the bias mitigation and consider the ethical concerns. These biases and ethical concerns may arise in models output. You need to implement measures to ensure fairness in inclusivity and responsible use. 16:28 Himanshu: The next phase in LLM life cycle is deployment. Once the model has been fine-tuned and evaluated, it is deployed for real world applications. Deployed models can perform tasks, such as text generation, translation, summarization, and much more. You also perform monitoring and maintenance in this phase. So you continuously monitor the model's performance and output to ensure it aligns with desired outcomes. You also periodically update and retrain the model to incorporate new data and to adapt to evolving language patterns. This overall life cycle can also consist of a feedback loop, whether you gather feedbacks from users and incorporate it into the model’s improvement process. You use this feedback to further refine prompts, fine tuning, and overall model behavior. RLHF, which is Reinforcement Learning with Human Feedback, is a very good example of this feedback loop. You also research and innovate as a part of this life cycle, where you continue to research and develop new techniques to enhance the model capability and address different challenges associated with it. 17:40 Nikita: As we’re talking about the LLM life cycle, I see that fine tuning is not only about making an LLM task specific. So, what are some other reasons you would fine tune an LLM model? Himanshu: The first one is task-specific adaptation. Pre-trained language models are trained on extensive and diverse data sets and have good general language understanding. They excel in language generation and comprehension tasks, though the broad understanding of language may not lead to optimal performance in specific task. These models are not task specific. So the solution is fine tuning. The fine tuning process customizes the pre-trained models for a specific task by further training on task-specific data to adapt the model's knowledge. The second reason is domain-specific vocabulary. Pre-trained models might lack knowledge of specific words and phrases essential for certain tasks in fields, such as legal, medical, finance, and technical domains. This can limit their performance when applied to domain-specific data. Fine tuning enables the model to adapt and learn domain-specific words and phrases. These words could be, again, from different domains. 18:56 Himanshu: The third reason to fine tune is efficiency and resource utilization. So fine tuning is computationally efficient compared to training from scratch. Fine tuning reuses the knowledge from pre-trained models, saving time and resources. Fine tuning requires fewer iterations to achieve task-specific competence. Shorter training cycles expedite the model development process. It conserves computational resources, such as GPU memory and processing power. Fine tuning is efficient in quicker model deployment. It has faster time to production for real world applications. Fine tuning is, again, scalable, enabling adaptation to various tasks with the same base model, which further reduce resource demands, and it leads to cost saving for research and development. The fourth reason to fine tune is of ethical concerns. Pre-trained models learns from diverse data. And those potentially inherit different biases. Fine tune might not completely eliminate biases. But careful curation of task-specific data ensures avoiding biased or harmful vocabulary. The responsible uses of domain-specific terms promotes ethical AI applications. 20:14 Lois: Thank you so much, Himanshu, for spending time with us. We had such a great time learning from you. If you want to learn... /episode/index/show/oracleuniversitypodcast/id/30836718

Encore Episode: Deep Learning 05/07/2024

Encore Episode: Deep Learning Did you know that the concept of deep learning goes way back to the 1950s? However, it is only in recent years that this technology has created a tremendous amount of buzz (and for good reason!). A subset of machine learning, deep learning is inspired by the structure of the human brain, making it fascinating to learn about. In this episode, Lois Houston and Nikita Abraham interview Senior Principal OCI Instructor Hemant Gahankari about deep learning concepts, including how Convolution Neural Networks work, and help you get your deep learning basics right. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:47 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! Lois: Today, we’re going to focus on the basics of deep learning with our Senior Principal OCI Instructor, Hemant Gahankari. Nikita: Hi Hemant! Thanks for being with us today. So, to get started, what is deep learning? 01:14 Hemant: Hi Niki and hi Lois. So, deep learning is a subset of machine learning that focuses on training Artificial Neural Networks, abbreviated as ANN, to solve a task at hand. Say, for example, image classification. A very important quality of the ANN is that it can process raw data like pixels of an image and extract patterns from it. These patterns are treated as features to predict the outcomes. Let us say we have a set of handwritten images of digits 0 to 9. As we know, everyone writes the digits in a slightly different way. So how do we train a machine to identify a handwritten digit? For this, we use ANN. ANN accepts image pixels as inputs, extracts patterns like edges and curves and so on, and correlates these patterns to predict an outcome. That is what digit does the image has in this case. 02:17 Lois: Ok, so what you’re saying is given a bunch of pixels, ANN is able to process pixel data, learn an internal representation of the data, and predict outcomes. That’s so cool! So, why do we need deep learning? Hemant: We need to specify features while we train machine learning algorithm. With deep learning, features are automatically extracted from the data. Internal representation of features and their combinations is built to predict outcomes by deep learning algorithms. This may not be feasible manually. Deep learning algorithms can make use of parallel computations. For this, usually data is split into small batches and processed parallelly. So these algorithms can process large amount of data in a short time to learn the features and their combinations. This leads to scalability and performance. In short, deep learning complements machine learning algorithms for complex data for which features cannot be described easily. 03:21 Nikita: What can you tell us about the origins of deep learning? Hemant: Some of the deep learning concepts like artificial neuron, perceptron, and multilayer perceptron existed as early as 1950s. One of the most important concept of using backpropagation for training ANN came in 1980s. In 1990s, convolutional neural networks were also introduced for image analysis tasks. Starting 2000, GPUs were introduced. And 2010 onwards, GPUs became cheaper and widely available. This fueled the widespread adoption of deep learning uses like computer vision, natural language processing, speech recognition, text translation, and so on. In 2012, major networks like AlexNet and Deep-Q Network were built. 2016 onward, generative use cases of the deep learning also started to come up. Today, we have widely adopted deep learning for a variety of use cases, including large language models and many other types of generative models. 04:32 Lois: Hemant, what are various applications of deep learning algorithms? Hemant: Deep learning algorithms are targeted at a variety of data and applications. For data, we have images, videos, text, and audio. For images, applications can be image classification, object detection, and segmentation. For textual data, applications are to translate the text or detect a sentiment of a text. For audio, the applications can be music generation, speech to text, and so on. 05:05 Lois: It's important that we select the right deep learning algorithm based on the data and application, right? So how do we do that? Hemant: For image tasks like image classification, object detection, image segmentation, or facial recognition, CNN is a suitable architecture. For text, we have a choice of the latest transformers or LSTM or even RNN. For generative tasks like text summarization or question answering, transformers is a good choice. For generating images, text to image generation, transformers, GANs, or diffusion models are available choices. 05:45 Nikita: Let’s dive a little deeper into Artificial Neural Networks. Can you tell us more about them, Hemant? Hemant: Artificial Neural Networks are inspired by the human brain. They are made up of interconnected nodes called as neurons. Nikita: And how are inputs processed by a neuron? Hemant: In ANN, we assign weights to the connection between neurons. Weighted inputs are added up. And if the sum crosses a specified threshold, the neuron is fired. And the outputs of a layer of neuron become an input to another layer. 06:16 Lois: Hemant, tell us about the building blocks of ANN so we understand this better. Hemant: So first building block is layers. We have input layer, output layer, and multiple hidden layers. The input layer and output layer are mandatory. And the hidden layers are optional. The layers consist of neurons. Neurons are computational units, which accept an input and produce an output. Weights determine the strength of connection between neurons. So the connections could be between input and a neuron, or it could be between a neuron and another neuron. Activation functions work on the weighted sum of inputs to the neuron and produce an output. Additional input to the neuron that allows a certain degree of flexibility is called as a bias. 07:05 Nikita: I think we’ve got the components of ANN straight but maybe you should give us an example. You mentioned this example earlier…of needing to train ANN to recognize handwritten digits from images. How would we go about that? Hemant: For that, we have to collect a large number of digit images, and we need to train ANN using these images. So, in this case, the images consist of 28 by 28 pixels, which act as input layer. For the output, we have 10 neurons which represent digits 0 to 9. And we have multiple hidden layers. So, for example, we have two hidden layers which are consisting of 16 neurons each. The hidden layers are responsible for capturing the internal representation of the raw images. And the output layer is responsible for producing the desired outcomes. So, in this case, the desired outcome is the prediction of whether the digit is 0 or 1 or up to digit 9. So how do we train this particular ANN? So the first thing we use is the backpropagation algorithm. During training, we show an image to the ANN. Let’s say it is an image of digit 2. So we expect output neuron for digit 2 to fire. But in real, let’s say output neuron of a digit 6 fired. 08:28 Lois: So, then, what do we do? Hemant: We know that there is an error. So we correct an error. We adjust the weights of the connection between neurons based on a calculation, which we call as backpropagation algorithm. By showing thousands of images and adjusting the weights iteratively, ANN is able to predict correct outcomes for most of the input images. This process of adjusting weights through backpropagation is called as model training. 09:01 Do you have an idea for a new course or learning opportunity? We’d love to hear it! Visit the Oracle University Learning Community and share your thoughts with us on the Idea Incubator. Your suggestion could find a place in future development projects! Visit mylearn.oracle.com to get started. 09:22 Nikita: Welcome back! Let’s move on to CNN. Hemant, what is a Convolutional Neural Network? Hemant: CNN is a type of deep learning model specifically designed for processing and analyzing grid-like data, such as images and videos. In the ANN, the input image is converted to a single dimensional array and given as an input to the network. But that does not work well with the image data because image data is inherently two dimensional. CNN works better with the two dimensional data. The role of the CNN is to reduce the images into a form, which is easier to process and without losing features, which are critical for getting a good prediction. 10:10 Lois: A CNN has different layers, right? Could you tell us a bit about them? Hemant: The first one is input layer. Input layer is followed by feature extraction layers, which is a combination and repetition of convolutional layer with ReLu activation and a pooling layer. And this is followed by a classification layer. These are the fully connected output layers, where the classification occurs as output classes. The class with the highest probability is the predicted class. And finally, we have the dropout layer. This layer is a regularization technique used to prevent overfitting in the network. 10:51 Nikita: And what are the top applications of CNN? Hemant: One of the most widely used applications of CNNs is image classification. For example, classifying whether an image contains a specific object, say cat or a dog. CNNs are also used for object detection tasks. The goal here is to draw bounding boxes around objects in an image. CNNs can perform pixel-level segmentation, where each pixel in the image is labeled to represent different objects or regions. CNNs are employed for face recognition tasks as well, identifying and verifying individuals based on facial features. CNNs are widely used in medical image analysis, helping with tasks like tumor detection, diagnosis, and classification of various medical conditions. CNNs play an important role in the development of self-driving cars, helping them to recognize and understand the road traffic signs, pedestrians, and other vehicles. 12:02 Nikita: Hemant, let’s talk about sequence models. What are they and what are they used for? Hemant: Sequence models are used to solve problems, where the input data is in the form of sequences. The sequences are ordered lists of data points or events. The goal in sequence models is to find patterns and dependencies within the data and make predictions, classifications, or even generate new sequences. 12:31 Lois: Can you give us some examples of sequence models? Hemant: Some common examples of the sequence models are in natural language processing, deep learning models are used for tasks, such as machine translation, sentiment analysis, or text generation. In speech recognition, deep learning models are used to convert a recorded audio into text. Deep learning models can generate new music or create original compositions. Even sequences of hand gestures are interpreted by deep learning models for applications like sign language recognition. In fields like finance or weather prediction, time series data is used to predict future values. 13:15 Nikita: Which deep learning models can be used to work with sequence data? Hemant: Recurrent Neural Networks, abbreviated as RNNs, are a class of neural network architectures specifically designed to handle sequential data. Unlike traditional feedforward neural network, RNNs have a feedback loop that allows information to persist across different timesteps. The key features of RNNs is their ability to maintain an internal state often referred to as a hidden state or memory, which is updated as the network processes each element in the input sequence. The hidden state is used as input to the network for the next time step, allowing the model to capture dependencies and patterns in the data that are spread across time. 14:07 Nikita: Are there various types of RNNs? Hemant: There are different types of RNN architectures based on application. One of them is one to one. This is like feed forward neural network and is not suited for sequential data. A one to many model produces multiple output values for one input value. Music generation or sequence generation are some applications using this architecture. A many to one model produces one output value after receiving multiple input values. Example is sentiments analysis based on the review. Many to many model produces multiple output values for multiple input values. Examples are machine translation and named entity recognition. RNN does not perform well when it comes to capturing long-term dependencies. This is due to the vanishing gradients problem, which is overcome by using LSTM model. 15:11 Lois: Another acronym. What is LSTM, Hemant? Hemant: Long Short-Term memory, abbreviated as LSTM, works by using a specialized memory cell and a gating mechanism to capture long term dependencies in the sequential data. The key idea behind LSTM is to selectively remember or forget information over time, enabling the model to maintain relevant information over long sequences, which helps overcome the vanishing gradients problem. 15:45 Nikita: Can you take us, step-by-step, through the working of LSTM? Hemant: At each timestep, the LSTM takes an input vector representing the current data point in the sequence. The LSTM also receives the previous hidden state and cell state. These represent what the LSTM has remembered and forgotten up to the current point in the sequence. The core of the LSTM lies in its gating mechanisms, which include three gates: the input gate, the forget gate, and the output gate. These gates are like the filters that control the flow of information within the LSTM cell. The input gate decides what new information from the current input should be added to the memory cell. The forget gate determines what information in the current memory cell should be discarded or forgotten. The output gate regulates how much of the current memory cell should be exposed as the output of the current time step. 16:52 Lois: Thank you, Hemant, for joining us in this episode of the Oracle University Podcast. I learned so much today. If you want to learn more about deep learning, visit mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. And remember, the AI Foundations course and certification are free. So why not get started now? Nikita: Right, Lois. In our next episode, we will discuss generative AI and language learning models. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 17:26 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/30836648

Encore Episode: Machine Learning 04/30/2024

Encore Episode: Machine Learning Does machine learning feel like too convoluted a topic? Not anymore! Listen to hosts Lois Houston and Nikita Abraham, along with Senior Principal OCI Instructor Hemant Gahankari, talk about foundational machine learning concepts and dive into how supervised learning, unsupervised learning, and reinforcement learning work. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:47 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence and we’re going to take it a step further today by talking about some foundational machine learning concepts. After that, we’ll discuss the three main types of machine learning models: supervised learning, unsupervised learning, and reinforcement learning. 01:18 Lois: Hemant Gahankari, a Senior Principal OCI Instructor, joins us for this episode. Hi Hemant! Let’s dive right in. What is machine learning? How does it work? Hemant: Machine learning is a subset of artificial intelligence that focuses on creating computer systems that can learn and predict outcomes from given examples without being explicitly programmed. It is powered by algorithms that incorporate intelligence into machines by automatically learning from a set of examples usually provided as data. 01:54 Nikita: Give us a few examples of machine learning… so we can see what it can do for us. Hemant: Machine learning is used by all of us in our day-to-day life. When we shop online, we get product recommendations based on our preferences and our shopping history. This is powered by machine learning. We are notified about movies recommendations based on our viewing history and choices of other similar viewers. This too is driven by machine learning. While browsing emails, we are warned of a spam mail because machine learning classifies whether the mail is spam or not based on its content. In the increasingly popular self-driving cars, machine learning is responsible for taking the car to its destination. 02:45 Lois: So, how does machine learning actually work? Hemant: Let us say we have a computer and we need to teach the computer to differentiate between a cat and a dog. We do this by describing features of a cat or a dog. Dogs and cats have distinguishing features. For example, the body color, texture, eye color are some of the defining features which can be used to differentiate a cat from a dog. These are collectively called as input data. We also provide a corresponding output, which is called as a label, which can be a dog or a cat in this case. By describing a specific set of features, we can say that it is a cat or a dog. Machine learning model is first trained with the data set. Training data set consists of a set of features and output labels, and is given as an input to the machine learning model. During the process of training, machine learning model learns the relation between input features and corresponding output labels from the provided data. Once the model learns from the data, we have a trained model. Once the model is trained, it can be used for inference. Inference is a process of getting a prediction by giving a data point. In this example, we input features of a cat or a dog, and the trained model predicts the output that is a cat or a dog label. The types of machine learning models depend on whether we have a labeled output or not. 04:28 Nikita: Oh, there are different types of machine learning models? Hemant: In general, there are three types of machine learning approaches. In supervised machine learning, labeled data is used to train the model. Model learns the relation between features and labels. Unsupervised learning is generally used to understand relationships within a data set. Labels are not used or are not available. Reinforcement learning uses algorithms that learn from outcomes to make decisions or choices. 05:06 Lois: Ok…supervised learning, unsupervised learning, and reinforcement learning. Where do we use each of these machine learning models? Hemant: Some of the popular applications of supervised machine learning are disease detection, weather forecasting, stock price prediction, spam detection, and credit scoring. For example, in disease detection, the patient data is input to a machine learning model, and machine learning model predicts if a patient is suffering from a disease or not. For unsupervised machine learning, some of the most common real-time applications are to detect fraudulent transactions, customer segmentation, outlier detection, and targeted marketing campaigns. So for example, given the transaction data, we can look for patterns that lead to fraudulent transactions. Most popular among reinforcement learning applications are automated robots, autonomous driving cars, and playing games. 06:12 Nikita: I want to get into how each type of machine learning works. Can we start with supervised learning? Hemant: Supervised learning is a machine learning model that learns from labeled data. The model learns the mapping between the input and the output. As a house price predictor model, we input house size in square feet and model predicts the price of a house. Suppose we need to develop a machine learning model for detecting cancer, the input to the model would be the person's medical details, the output would be whether the tumor is malignant or not. 06:50 Lois: So, that mapping between the input and output is fundamental in supervised learning. Hemant: Supervised learning is similar to a teacher teaching student. The model is trained with the past outcomes and it learns the relationship or mapping between the input and output. In supervised machine learning model, the outputs can be either categorical or continuous. When the output is continuous, we use regression. And when the output is categorical, we use classification. 07:26 Lois: We want to keep this discussion at a high level, so we’re not going to get into regression and classification. But if you want to learn more about these concepts and look at some demonstrations, visit mylearn.oracle.com. Nikita: Yeah, look for the Oracle Cloud Infrastructure AI Foundations course and you’ll find a lot of resources that you can make use of. 07:51 The Oracle University Learning Community is an excellent place to collaborate and learn with Oracle experts and fellow learners. Grow your skills, inspire innovation, and celebrate your successes. All your activities, from liking a post to answering questions and sharing with others, will help you earn a valuable reputation, badges, and ranks to be recognized in the community. Visit mylearn.oracle.com to get started. 08:19 Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Hemant? Hemant: Unsupervised machine learning is a type of machine learning where there are no labeled outputs. The algorithm learns the patterns and relationships in the data and groups similar data items. In unsupervised machine learning, the patterns in the data are explored explicitly without being told what to look for. For example, if you give a set of different-colored LEGO pieces to a child and ask to sort it, it may the LEGO pieces based on any patterns they observe. It could be based on same color or same size or same type. Similarly, in unsupervised learning, we group unlabeled data sets. One more example could be-- say, imagine you have a basket of various fruits-- say, apples, bananas, and oranges-- and your task is to group these fruits based on their similarities. You observe that some fruits are round and red, while others are elongated and yellow. Without being told explicitly, you decide to group the round and red fruits together as one cluster and the elongated and yellow fruits as another cluster. There you go. You have just performed an unsupervised learning task. 09:42 Lois: Where is unsupervised machine learning used? Can you take us through some use cases? Hemant: The first use case of unsupervised machine learning is market segmentation. In market segmentation, one example is providing the purchasing details of an online shop to a clustering algorithm. Based on the items purchased and purchasing behavior, the clustering algorithm can identify customers based on the similarity between the products purchased. For example, customers with a particular age group who buy protein diet products can be shown an advertisement of sports-related products. The second use case is on outlier analysis. One typical example for outlier analysis is to provide credit card purchase data for clustering. Fraudulent transactions can be detected by a bank by using outliers. In some transaction, amounts are too high or recurring. It signifies an outlier. The third use case is recommendation systems. An example for recommendation systems is to provide users' movie viewing history as input to a clustering algorithm. It clusters users based on the type or rating of movies they have watched. The output helps to provide personalized movie recommendations to users. The same applies for music recommendations also. 11:14 Lois: And finally, Hemant, let’s talk about reinforcement learning. Hemant: Reinforcement learning is like teaching a dog new tricks. You reward it when it does something right, and over time, it learns to perform these actions to get more rewards. Reinforcement learning is a type of Machine Learning that enables an agent to learn from its interaction with the environment, while receiving feedback in the form of rewards or penalties without any labeled data. Reinforcement learning is more prevalent in our daily lives than we might realize. The development of self-driving cars and autonomous drones rely heavily on reinforcement learning to make real time decisions based on sensor data, traffic conditions, and safety considerations. Many video games, virtual reality experiences, and interactive entertainment use reinforcement learning to create intelligent and challenging computer-controlled opponents. The AI characters in games learn from player interactions and become more difficult to beat as the game progresses. 12:25 Nikita: Hemant, take us through some of the terminology that’s used with reinforcement learning. Hemant: Let us say we want to train a self-driving car to drive on a road and reach its destination. For this, it would need to learn how to steer the car based on what it sees in front through a camera. Car and its intelligence to steer on the road is called as an agent. More formally, agent is a learner or decision maker that interacts with the environment, takes actions, and learns from the feedback received. Environment, in this case, is the road and its surroundings with which the car interacts. More formally, environment is the external system with which the agent interacts. It is the world or context in which the agent operates and receives feedback for its actions. What we see through a camera in front of a car at a moment is a state. State is a representation of the current situation or configuration of the environment at a particular time. It contains the necessary information for the agent to make decisions. The actions in this example are to drive left, or right, or keep straight. Actions are a set of possible moves or decisions that the agent can take in a given state. Actions have an impact on the environment and influence future states. After driving through the road many times, the car learns what action to take when it views a road through the camera. This learning is a policy. Formally, policy is a strategy or mapping that the agent uses to decide which action to take in a given state. It defines the agent's behavior and determines how it selects actions. 14:13 Lois: Ok. Say we’re talking about the training loop of reinforcement learning in the context of training a dog to learn tricks. We want it to pick up a ball, roll, sit… Hemant: Here the dog is an agent, and the place it receives training is the environment. While training the dog, you provide a positive reward signal if the dog picks it right and a warning or punishment if the dog does not pick up a trick. In due course, the dog gets trained by the positive rewards or negative punishments. The same tactics are applied to train a machine in the reinforcement learning. For machines, the policy is the brain of our agent. It is a function that tells what actions to take when in a given state. The goal of reinforcement learning algorithm is to find a policy that will yield a lot of rewards for the agent if the agent follows that policy referred to as the optimal policy. Through a process of learning from experiences and feedback, the agent becomes more proficient at making good decisions and accomplishing tasks. This process continues until eventually we end up with the optimal policy. The optimal policy is learned through training by using algorithms like Deep Q Learning or Q Learning. 15:40 Nikita: So through multiple training iterations, it gets better. That’s fantastic. Thanks, Hemant, for joining us today. We’ve learned so much from you. Lois: Remember, the course and certification are free, so if you’re interested, make sure you log in to mylearn.oracle.com and get going. Join us next week for another episode of the Oracle University Podcast. Until then, I’m Lois Houston… Nikita: And Nikita Abraham signing off! 16:09 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/30836503

Encore Episode: Introduction to Artificial Intelligence (AI) 04/23/2024

Encore Episode: Introduction to Artificial Intelligence (AI) You probably interact with artificial intelligence (AI) more than you realize. So, there’s never been a better time to start figuring out how it all works. Join Lois Houston and Nikita Abraham as they decode the fundamentals of AI so that anyone, irrespective of their technical background, can leverage the benefits of AI and tap into its infinite potential. Together with Senior Cloud Engineer Nick Commisso, they take you through key AI concepts, common AI tasks and domains, and the primary differences between AI, machine learning, and deep learning. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:46 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Welcome to a new season of the Oracle University Podcast. I’m so excited about this season because we’re going to delve into the world of artificial intelligence. In upcoming episodes, we’ll talk about the fundamentals of artificial intelligence and machine learning. And we’ll discuss neural network architectures, generative AI and large language models, the OCI AI stack, and OCI AI services. 01:27 Nikita: So, if you’re an IT professional who wants to start learning about AI and ML or even if you’re a student who is familiar with OCI or similar cloud services, but have no prior exposure to this field, you’ll want to tune in to these episodes. Lois: That’s right, Niki. So, let’s get started. Today, we’ll talk about the basics of artificial intelligence with Senior Cloud Engineer Nick Commisso. Hi Nick! Thanks for joining us today. So, let’s start right at the beginning. What is artificial intelligence? 01:57 Nick: Well, the ability of machines to imitate the cognitive abilities and problem solving capabilities of human intelligence can be classified as artificial intelligence or AI. 02:08 Nikita: Now, when you say capabilities and abilities, what are you referring to? Nick: Human intelligence is the intellectual capability of humans that allows us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using a language and understand the nonverbal cues, such as facial recognition, tone variation, and body language. You can handle objections in real time, even in a complex setting. You can plan for short and long-term situations or projects. And, of course, you can create music and art or invent something new like an original idea. If you can replicate any of these human capabilities in machines, this is artificial general intelligence or AGI. So in other words, AGI can mimic human sensory and motor skills, performance, learning, and intelligence, and use these abilities to carry out complicated tasks without human intervention. When we apply AGI to solve problems with specific and narrow objectives, we call it artificial intelligence or AI. 03:16 Lois: It seems like AI is everywhere, Nick. Can you give us some examples of where AI is used? Nick: AI is all around us, and you've probably interacted with AI, even if you didn't realize it. Some examples of AI can be viewing an image or an object and identifying if that is an apple or an orange. It could be examining an email and classifying it spam or not. It could be writing computer language code or predicting the price of an older car. So let's get into some more specifics of AI tasks and the nature of related data. Machine learning, deep learning, and data science are all associated with AI, and it can be confusing to distinguish. 03:57 Nikita: Why do we need AI? Why’s it important? Nick: AI is vital in today's world, and with the amount of data that's generated, it far exceeds the human ability to absorb, interpret, and actually make decisions based on that data. That's where AI comes in handy by enhancing the speed and effectiveness of human efforts. So here are two major reasons why we need AI. Number one, we want to eliminate or reduce the amount of routine tasks, and businesses have a lot of routine tasks that need to be done in large numbers. So things like approving a credit card or a bank loan, processing an insurance claim, recommending products to customers are just some example of routine tasks that can be handled. And second, we, as humans, need a smart friend who can create stories and poems, designs, create code and music, and have humor, just like us. 04:54 Lois: I’m onboard with getting help from a smart friend! There are different domains in AI, right, Nick? Nick: We have language for language translation; vision, like image classification; speech, like text to speech; product recommendations that can help you cross-sell products; anomaly detection, like detecting fraudulent transactions; learning by reward, like self-driven cars. You have forecasting with weather forecasting. And, of course, generating content like image from text. 05:24 Lois: There are so many applications. Nick, can you tell us more about these commonly used AI domains like language, audio, speech, and vision? Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, or extracting key phrases and so on. Consider the example of translating text. There's many text translation tools where you simply type or paste your text into a given text box, choose your source and target language, and then click translate. Now, let's look at the generative AI tasks. They are generative, which means the output text is generated by a model. Some examples are creating text like stories or poems, summarizing a text, answering questions, and so on. Let's take the example of ChatGPT, the most well-known generative chat bot. These bots can create responses from their training on large language models, and they continuously grow through machine learning. 06:31 Nikita: What can you tell us about using text as data? Nick: Text is inherently sequential, and text consists of sentences. Sentences can have multiple words, and those words need to be converted to numbers for it to be used to train language models. This is called tokenization. Now, the length of sentences can vary, and all the sentences lengths need to be made equal. This is done through padding. Words can have similarities with other words, and sentences can also be similar to other sentences. The similarity can be measured through dot similarity or cosine similarity. We need a way to indicate that similar words or sentences may be close by. This is done through representation called embedding. 07:17 Nikita: And what about language AI models? Nick: Language AI models refer to artificial intelligence models that are specifically designed to understand, process, and generate natural language. These models have been trained on vast amounts of textual data that can perform various natural language processing or NLP tasks. The task that needs to be performed decides the type of input and output. The deep learning model architectures that are typically used to train models that perform language tasks are recurrent neural networks, which processes data sequentially and stores hidden states, long short-term memory, which processes data sequentially that can retain the context better through the use of gates, and transformers, which processes data in parallel. It uses the concept of self-attention to better understand the context. 08:09 Lois: And then there’s speech-related AI, right? Nick: Speech-related AI tasks can be either audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech-to-text conversion or speaker recognition, voice conversion, and so on. Generative AI tasks are generative in nature, so the output audio is generated by a model. For example, you have music composition and speech synthesis. Audio or speech is digitized as snapshots taken in time. The sample rate is the number of times in a second an audio sample is taken. Most digital audio have a sampling rate of 44.1 kilohertz, which is also the sampling rate for audio CDs. Multiple samples need to be correlated to make sense of the data. For example, listening to a song for a fraction of a second, you won't be able to infer much about the song, and you'll probably need to listen to it a little bit longer. Audio and speech AI models are designed to process and understand audio data, including spoken language. These deep-learning model architectures are used to train models that perform language with tasks-- recurrent neural networks, long short-term memory, transformers, variational autoencoders, waveform models, and Siamese networks. All of the models take into consideration the sequential nature of audio. 09:42 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started. 10:10 Nikita: Welcome back! Now that we’ve covered language and speech-related tasks, let’s move on to vision-related tasks. Nick: Vision-related AI tasks could be image related or generative AI. Image-related AI tasks will use an image as an input, and the output depends on the task. Some examples are classifying images, identifying objects in an image, and so on. Facial recognition is one of the most popular image-related tasks that is often used for surveillance and tracking of people in real time, and it's used in a lot of different fields, including security, biometrics, law enforcement, and social media. For generative AI tasks, the output image is generated by a model. For example, creating an image from a contextual description, generating images of a specific style or a high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of an object, machine components, buildings, medication, people, and even more. 11:14 Lois: So, then, here again I need to ask, how do images work as data? Nick: Images consist of pixels, and pixels can be either grayscale or color. And we can't really make out what an image is just by looking at one pixel. The task that needs to be performed decides the type of input needed and the output produced. Various architectures have evolved to handle this wide variety of tasks and data. These deep-learning model architectures are typically used to train models that perform vision tasks-- convolutional neural networks, which detects patterns in images; learning hierarchical representations of visual features; YOLO, which is You Only Look Once, processes the image and detects objects within the image; and then you have generative adversarial networks, which generates real-looking images. 12:04 Nikita: Nick, earlier you mentioned other AI tasks like anomaly detection, recommendations, and forecasting. Could you tell us more about them? Nick: Anomaly detection. This is time-series data, which is required for anomaly detection, and it can be a single or multivariate for fraud detection, machine failure, etc. Recommendations. You can recommend products using data of similar products or users. For recommendations, data of similar products or similar users is required. Forecasting. Time-series data is required for forecasting and can be used for things like weather forecasting and predicting the stock price. 12:43 Lois: Nick, help me understand the difference between artificial intelligence, machine learning, and deep learning. Let’s start with AI. Nick: Imagine a self-driving car that can make decisions like a human driver, such as navigating traffic or detecting pedestrians and making safe lane changes. AI refers to the broader concept of creating machines or systems that can perform tasks that typically require human intelligence. Next, we have machine learning or ML. Visualize a spam email filter that learns to identify and move spam emails to the spam folder, and that's based on the user's interaction and email content. Now, ML is a subset of AI that focuses on the development of algorithms that enable machines to learn from and make predictions or decisions based on data. To understand what an algorithm is in the context of machine learning, it refers to a specific set of rules, mathematical equations, or procedures that the machine learning model follows to learn from data and make predictions on. And finally, we have deep learning or DL. Think of an image recognition software that can identify specific objects or animals within images, such as recognizing cats in photos on the internet. DL is a subfield of ML that uses neural networks with many layers, deep neural networks, to learn and make sense of complex patterns in data. 14:12 Nikita: Are there different types of machine learning? Nick: There are several types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning where the algorithm learns from labeled data, making predictions or classifications. Unsupervised learning is an algorithm that discovers patterns and structures in unlabeled data, such as clustering or dimensionality reduction. And then, you have reinforcement learning, where agents learn to make predictions and decisions by interacting with an environment and receiving rewards or punishments. 14:47 Lois: Can we do a deep dive into each of these types you just mentioned? We can start with the supervised machine learning algorithm. Nick: Let's take an example of how a credit card company would approve a credit card. Once the application and documents are submitted, a verification is done, followed by a credit score check and another 10 to 15 days for approval. And how is this done? Sometimes, purely manually or by using a rules engine where you can build rules, give new data, get a decision. The drawbacks are slow. You need skilled people to build and update rules, and the rules keep changing. The good thing is that the businesses had a lot of insight as to how the decisions were made. Can we build rules by looking at the past data? We all learn by examples. Past data is nothing but a set of examples. Maybe reviewing past credit card approval history can help. Through a process of training, a model can be built that will have a specific intelligence to do a specific task. The heart of training a model is an algorithm that incrementally updates the model by looking at the data samples one by one. And once it's built, the model can be used to predict an outcome on a new data. We can train the algorithm with credit card approval history to decide whether to approve a new credit card. And this is what we call supervised machine learning. It's learning from labeled data. 16:13 Lois: Ok, I see. What about the unsupervised machine learning algorithm? Nick: Data does not have a specific outcome or a label as we know it. And sometimes, we want to discover trends that the data has for potential insights. Similar data can be grouped into clusters. For example, retail marketing and sales, a retail company may collect information like household size, income, location, and occupation so that the suitable clusters could be identified, like a small family or a high spender and so on. And that data can be used for marketing and sales purposes. Regulating streaming services. A streaming service may collect information like viewing sessions, minutes per session, number of unique shows watched, and so on. That can be used to regulate streaming services. Let's look at another example. We all know that fruits and vegetables have different nutritional elements. But do we know which of those fruits and vegetables are similar nutritionally? For that, we'll try to cluster fruits and vegetables' nutritional data and try to get some insights into it. This will help us include nutritionally different fruits and vegetables into our daily diets. Exploring patterns and data and grouping similar data into clusters drives unsupervised machine learning. 17:34 Nikita: And then finally, we come to the reinforcement learning algorithm. Nick: How do we learn to play a game, say, chess? We'll make a move or a decision, check to see if it's the right move or feedback, and we'll keep the outcomes in your memory for the next step you take, which is learning. Reinforcement learning is a machine learning approach where a computer program learns to make decisions by trying different actions and receiving feedback. It teaches agents how to solve tasks by trial and error. This approach is used in autonomous car driving and robots as well. 18:06 Lois: We keep coming across the term “deep learning.” You’ve spoken a bit about it a few times in this episode, but what is deep learning, really? How is it related to machine learning? Nick: Deep learning is all about extracting features and rules from data. Can we identify if an image is a cat or a dog by looking at just one pixel? Can we write rules to identify a cat or a dog in an image? Can the features and rules be extracted from the raw data, in this case, pixels? Deep learning is really useful in this situation. It's a special kind of machine learning that trains super smart computer networks with lots of layers. And these networks can learn things all by themselves from pictures, like figuring out if a picture is a cat or a dog. 18:49 Lois: I know we’re going to be covering this in detail in an upcoming episode, but before we let you go, can you briefly tell us about generative AI? Nick: Generative AI, a subset of machine learning, creates diverse content like text, audio, images, and more. These models, often powered by neural networks, learn patterns from existing data to craft fresh and creative output. For instance, ChatGPT generates text-based responses by understanding patterns in text data that it's been trained on. Generative AI plays a vital role in various AI tasks requiring content creation and innovation. 19:28 Nikita: Thank you, Nick, for sharing your expertise with us. To learn more about AI, go to mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. As you complete the course, you’ll find skill checks that you can attempt to solidify your learning. Lois: And remember, the AI Foundations course on MyLearn also prepares you for the... /episode/index/show/oracleuniversitypodcast/id/30836103

Using ORDS to Make Your ADB Data Available in VBS 04/16/2024

Using ORDS to Make Your ADB Data Available in VBS Visual Builder Studio requires its data sources to connect to the webpage it produces using REST calls. Therefore, the data source has to provide a REST interface. A simple, easy, secure, and free way to do that is with Oracle REST Data Services (ORDS). In this episode, hosts Lois Houston and Nikita Abraham chat with Senior Principal OCI Instructor Joe Greenwald about what ORDS can do, how to easily set it up, how to work with it, and how to use it within Visual Builder Studio. Develop Fusion Applications Using Visual Builder Studio: Build Visual Applications Using Visual Builder Studio: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we took a look at model-based development tools, their start as CASE tools, what they morphed into, and how they’re currently used in Oracle software development. We’re wrapping up the season with this episode today, which will be about how to access Oracle database data through a REST interface created and managed by Oracle REST Data Services, or ORDS, and how to access this data in Visual Builder Studio. 01:03 Nikita: Being able to access Oracle database data through a REST interface over the web is highly useful, but sometimes it can be complicated to create that interface in a programming language. Joe Greenwald, our Senior OCI Learning Solutions Architect and Principal Instructor is back with us one last time this season to tell us more about ORDS, and how it makes it much simpler and easier for us to REST-enable our database for use in tools like Visual Builder Studio. Hi Joe! Tell us a little about what Visual Builder Studio is and why we must REST-enable our data for VBS to be able to use it. 01:40 Joe: Hi Niki, hi Lois! Ok, so, Visual Builder Studio is Oracle’s low-code software development and project asset management product for creating graphical webpage front-ends for web applications. It’s the tool of choice for designing, building, and implementing all of Oracle Fusion Cloud Applications and is being used by literally tens of thousands of engineers at Oracle now to bring the next generation of Fusion Applications to our customers and the market. It’s based on standards like HTML5, CSS3, and JavaScript. It’s highly performant and combined with the Redwood graphical design system and components that we talked about previously, delivers a world-class experience for users. One thing about Visual Builder Studio though: it only works with data sources that have a REST interface. This is unusual. I like to think I’ve worked with every software development tool that Oracle’s created since I joined Oracle in 1992, including some unreleased ones, and all of them allowed you to talk to the database directly. This is the first time that we’ve released a tool that I know of where we don’t do that. Now at first, I was a little put off and wondered how’s it going to do this and how much work I would have to do to create a REST interface for some simple tables in the Oracle database. Like, here’s one more thing I must do just to create a page that displays data from the database. As it turns out, it’s a wise design decision on the part of the designers. It simplifies the data access parts of Visual Builder Studio and makes the data access model common across the different data sources. And, thanks to ORDS, REST-enabling data in Oracle database couldn’t be easier! 03:13 Lois: That’s cool. We don’t want to focus too much on Visual Builder Studio today. We have free courses that teach you how to create service connections to REST services to access the data and all of that. What we actually want to talk with you about is working with Oracle REST Data Service. How easy is it to work with Oracle REST Data Service to add REST support, what we call REST-enable your Oracle Database, and why it is important? Nikita: Yeah, I could use a bit of a refresher on REST myself. Could you describe what REST is, how it works for both the client and server, and what ORDS is doing for us? 03:50 Joe: Sure. So, REST is a way to make a request to a server for a resource using the HTTP web protocol from a client, like your browser, to a web server, which hands off the request to code that handles the request and sends the response back to your client/browser, which then uses it, displays or whatever. So, you can see we have two parts. We have the client, which makes the request, and the server, which handles the request and figures out what the response should be (static, dynamic, or a combination of both) and sends that back to the client. For example, a visual application built with Visual Builder Studio acts as a client making the request, just as your browser makes a request. It’s really just a web app built with HTML5, CSS3, and JavaScript, and the JavaScript makes a request to the server on your behalf. Let’s say you wish to access your student record within Oracle University. And now, this is a contrived example and it won’t actually work, but it’s good for illustrative purposes. Oracle University, let’s say, publishes the URL for your student data as something like https://oracle.com/oracleuniversity/student/{studentnumber} you put in some kind of number, like the number 23, and if you enter that into your browser address bar and press Enter, then your browser, on your behalf, sends a GET request—what we call an HTTP GET operation—to the web server. When the web server receives the request, it will somehow read the record for student 23, format a response, and send the response back to the client. 05:16 Joe: That’s a GET or a READ request. Now, what if you are creating a new student? Well, you fill out a form on the webpage and you click the Submit button. And it sends a POST request, which tells the server to create a new record in its storage mechanism, most likely a database of some form. If you do an update, you change certain fields on the webpage and click the Submit button and this time, an update request is made. If you wanted to delete the record, you’d find the record you want to delete and press the Submit button, and this time a delete request is made. This is the general idea, though there are different ways to do creates and updates that are really irrelevant here. Those requests to the server I mentioned are called HTTP operations and there are several of them. But the four most popular are GET to retrieve data, POST to create a new record on the server, PUT to update a record, and DELETE to remove a record. On the client side, we just need to specify where the record is that we want to retrieve—that’s the oracle.com/oracleuniversity/student part of the URL and an identifying value, which makes it unique. So, when I do a GET request on customer or student 23, I’m going to get back a representation of the student data that exists in the student database for a student with ID 23. There should not be more than one of these or that would indicate an error. The response typically comes back in a format of a key:value pair called JavaScript Object Notation (JSON), but it could also be in a Text format, HTML, Excel, PDF, or whatever the server implements and is requested. 06:42 Nikita: OK great! That’s on the client side making the request. But what’s happening on the server side? Do I need to worry about that if I’m the client? Joe: No, that’s the great part. As a client, I don’t know, and frankly I’d rather not know what the server’s doing or how it does it. I don’t want to be dependent on the server implementation at all. I simply want to make a request and the server handles the request and sends a response. Now, just a word about what’s on the server. Some data on the server is static like a PDF file or an image or an audio file, for example, and sometimes you’ll see that in the URL the file type as an extension, like .pdf, and you get back a PDF file that your browser displays or that you can download to your machine. But with dynamic data, like student data coming out of a database based on the student number, a query is made against a database. The database responds with the data, and that’s formatted into some type of data format—typically JSON—and sent back to the client, which then does something with it, like displaying it on a webpage. So, as we can see, the client is fairly simple in the sense that it makes a request, receives the data response, and displays it or does something with it. And that’s one of the reasons why the choice to use REST and only REST in Visual Builder Studio is such a wise one. 07:54 Joe: Regardless of the different data sources or the different server implementations or how the data is stored on the server, or any of that, Visual Builder Studio doesn’t know and doesn’t care. What it sees is the REST request it sends and the response it gets back and then it deals with the response data regardless of how it’s implemented on the server. I mentioned the server sends back a representation of the resource, in this case, for example, the student record. That’s really where the abbreviation REST comes from: REpresentational State Transformation, which is a long way of saying, bring me back a representation of the resource—the thing—that I’m requesting. Now, of course, the server is a little more complex. On the server side, we would need software that is going to take the request from the web server using some programming language like Java, C#, C++, Python, or maybe even JavaScript in a Node.js application. You have a program that receives a request from the web server, executes the request (typically by connecting to the database if it’s a database call), makes the request, receives a data response from the database, formats that into some form, and passes it back to the web server, which then sends it back to the client that requested it. 09:01 Lois: Ok… I think I see. I’m guessing that ORDS gets involved somehow between the client and the server. Joe: Yes, exactly. We can see that the implementation on the server side is where the complexity is. For example, if I implement a student management service in Java, I have to write a bunch of Java code, a lot of which is boilerplate, housekeeping, boring code. For simple database access, it’s tedious to have to do this over and over, and if the database changes, it can be even more tedious to maintain that code to handle simple to moderately complex requests. Writing and maintaining software code to just read and write data from the database to pass to a client for a web request is cool the very first time you do it and then gets boring very quickly and it’s prone to errors because it’s so manual. So, it would be nice if we had a piece of software that could handle the tedious, boring, manual bits of this service. It would receive the request that our client, the browser or Visual Builder Studio for example, is sending, take that request, execute the request against the database for us, receive the response from the database, and then format it for us and send it back to us, without a developer having to write custom code on the server side. And that is what Oracle REST Data Services (ORDS) does. 10:13 Joe: ORDS contains a lightweight web server based on the Jetty web server that receives the request from the client, like a browser or Visual Builder Studio or whatever, in the form of a URL, parses the request, generates a query or an update, or an insert or delete, depending on the nature of the HTTP operation sent or requested, and sends it to the database on our behalf. The database executes the request from ORDS, sends back a response to ORDS, and ORDS formats the response for us in the JSON and sends it back to our client. In nutshell, that’s it. 10:45 Lois: So ORDS does all that? And it’s free? How does it work? Uhm, remember I’m not as technical as you are. Joe: Of course. ORDS is free. It’s a lightweight, highly performant Java app that can run in many different modes, from stand-alone on a server to embedded in an application server like WebLogic, to running in the Oracle Cloud with the Oracle Autonomous Database (ADB). When you REST-enable your tables, your web requests are intercepted by ORDS running in ADB. It’s optimized for the purpose of handling web requests, connecting to the Oracle database, and sending back formatted responses as JSON. It can also handle more complex requests as well in the form of queries with special parameters. So, you can see what ORDS does for us. It handles the request coming from the client, which could be a browser or Visual Builder Studio or APEX or whatever client—pretty much any client today can make an HTTP call—it handles the call, parses the request, makes the request to the server on our behalf, and of course security is built-in and all of that, and so we don’t get to data we’re not supposed to see. It receives a response from the database, formats it into the JSON key:value pair format, and sends it back to our client. 12:00 Are you planning to become an Oracle Certified Professional this year? Whether you're a seasoned IT pro or just starting your career, getting certified can give you a significant boost. And don't worry, we've got your back. Join us at one of our cert prep live events in the Oracle University Learning Community. You'll get insider tips from seasoned experts and learn from other professionals' experiences. Plus, once you've earned your certification, you'll become part of our exclusive forum for Oracle-certified users. So, what are you waiting for? Head over to mylearn.oracle.com and create an account to jump-start your journey towards certification today! 12:43 Nikita: Welcome back. So, Joe, then the next question is, what do we do to REST-enable our database? Does that only work for ADB? Joe: This can be done in a couple of different ways. It can be done implicitly, called AutoREST, or explicitly. AutoREST is very convenient. In the case of an ADB database, you log in as the user who owns the structures, select your tables, views, packages, procedures, or functions that you want to REST-enable. Choose REST and then Enable from the menu for the table, view, stored package, procedure, or function and a URL is generated using your POST, GET, PUT, and DELETE for the standard database create, retrieve, update, delete operations. And it’s not just for ADB. You can do this in SQL Developer Desktop as well. Then, when you invoke the URL for the service, if you include just the name of the resource, like students, you get the entire collection back. If you add an ID at the end of the URL, like student/23, you get back the data for that specific student back, or whatever the structure is. You can add more complex filter parameters as well. And that’s it! Very easy. And, of course, you can apply appropriate security and ORDS enforces it. But you also can create custom code to handle more complex requests. 13:53 Lois: Joe, what if there’s custom logic or processing that you want to do when the REST call comes in and you need to write custom code to handle it? Joe: Remember, I said on the server side, we use custom code to retrieve data as well as apply business rules, validations, edits, whatever needs to be done to appropriately handle the REST call. So it’s a great question, Lois. When using ORDS, you can write a REST service handler in PL/SQL and SQL, just like if you were writing a stored procedure or a function or a package in the database, which is exactly what you’re doing. ORDS exposes your PL/SQL code wrapped in a REST interface with, of course, the necessary security. And since it’s PL/SQL, it runs in the database, so it’s highly performant, fast, and uses code you’re likely already familiar with or maybe already have. Your REST service handler can call existing PL/SQL packages, procedures, and functions. For example, if you created packages with stored procedures and functions that wrap access to your database tables and views, you can REST-enable those stored procedures, functions, and packages, and call them over the web. And maintain the package access you already created. I do want to point out that the recommended way to access your tables and views is through packages, stored procedures, and functions. While you can expose your tables and views directly to REST, should you really do that? In practice, it’s generally not a recommended way to do it. Do you want to expose your data in tables and views directly through a REST interface? Ideally, no, access should be through a PL/SQL wrapper, same as it’s—hopefully—done today for your client-server applications. 15:26 Nikita: I understand it’s easy to generate a simple REST interface for tables and so on to do basic create, retrieve, update, and delete operations. But what’s required to create custom code to handle more complex business operations? Joe: The process to create your own custom handlers is a little bit more involved as you would expect. It uses your skills as a PL/SQL programmer, while hiding the details of the REST implementation to let you focus on the logic and processing. Mechanically, you’d begin by creating a module that has a URL associated with it. So, for example, you would create a URL like https://oracle.com/oracleuniversity/studentregistry. Then, within that module, you create a template that names the specific resource—or thing—that you want to work with. For example, student, or course, or registration. 16:15 Joe: Then you create the handler for it. You have a handler to do the read, another handler for the insert, another handler for an update, another handler for a delete, and even possibly multiple handlers for more complex APIs based on your needs and the parameters being passed in. You can create complex URLs with multiple parameters for passing needed information into the PL/SQL procedure, which is going to do the actual programming work for you. There are predefined implicit variables about the message itself that you can use, as well as all the parameters from the URL itself. Now, this is all done in a nice developer interface on the web if you’re using SQL Developer Web with ADB or in SQL Developer for the desktop. Either one can do this because under the covers, ORDS is generating and executing the PL/SQL calls necessary to create and expose your web services. It’s very easy to work with and test immediately. 17:06 Lois: Joe, how much REST knowledge do I need to use ORDS properly to create REST services? Joe: Well, you should have some basic knowledge of REST, HTTP operations, request and response messages, and JSON, since this is the data format ORDS produces. The developer interface is really not designed for somebody who knows nothing about REST at all; it’s not designed to take them step-by-step through everything that needs to be done. It’s not wizard-based. Rather, it’s an efficient, minimal interface that can be used quickly and easily by someone who has at least some experience building REST services. But, if you have a little knowledge and you understand how... /episode/index/show/oracleuniversitypodcast/id/30750243

Forgotten, But Not Gone: How Model-Based Development Is Still Alive and Well Today 04/09/2024

Forgotten, But Not Gone: How Model-Based Development Is Still Alive and Well Today Computer Aided Software Engineering (CASE) tools, which helped make the analysis, design, and implementation phases of software development better, faster, and cheaper, fell out of favor in the mid-'90s. Yet much of what they have to offer remains and is in active use within different Oracle tools. Listen to Lois Houston and Nikita Abraham interview Senior Principal OCI Instructor Joe Greenwald about the origins of CASE tools and model-based development, as well as how they evolved into their current forms. Develop Fusion Applications Using Visual Builder Studio: Build Visual Applications Using Visual Builder Studio: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. --------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and joining me is Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we looked at Oracle’s Redwood design system and how it helps create world-class apps and user experiences. Today, Joe Greenwald, our Senior Principal OCI Instructor, is back on our podcast. We’re going to focus on where model-based development tools came from: their start as CASE tools, how they morphed into today’s model-based development tools, and how these tools are currently used in Oracle software development to make developers’ lives better. 01:08 Nikita: That’s right. It’s funny how things that fell out of favor years ago come back and are used to support our app development efforts today. Hi Joe! Joe: Haha! Hi Niki. Hi Lois.  01:18 Lois: Joe, how did you get started with CASE tools? Joe: I was first introduced to computer-aided software engineering tools, called CASE tools, in the late 1980s when I began working with them at Arthur Young consulting and then Knowledgeware corporation in Atlanta, helping customers improve and even automate their software development efforts using structured analysis and design techniques, which were popular and in high use at that time. But it was a pain to have to draw diagrams by hand, redraw them as specifications changed, and then try to maintain them to represent the changes in understanding what we were getting from our analysis and design phase work. CASE tools were used to help us draw the pictures as well as enforce rules and provide a repository so we could share what we were creating with other developers. I was immediately attracted to the idea of using diagrams and graphical images to represent requirements for computer systems. 02:08 Lois: Yeah, you’re like me. You’re a visual person. Joe: Yes, exactly. So, the idea that I could draw a picture and a computer could turn that into executable code was fascinating to me. Pictures helped us understand what the analysts told us the users wanted, and helped us communicate amongst the teams, and they also helped us validate our understanding with our users. This was a critical aspect because there was a fundamental cognitive disconnect between what the users told the analysts they needed, what the analysts told us the users needed, and what we understood was needed, and what the user actually wanted. There’s a famous cartoon, you can probably find this on the web, that shows what the users wanted, what was delivered, and then all the iterations that the different teams go through trying to represent the simple original request. I started using entity relationship diagrams, data flow diagrams, and structure charts to support the structured analysis, design, and information engineering methods that we were using at the time for our clients. Used correctly, these were powerful tools that resulted in higher quality systems because it forced us to answer questions earlier on and not wait until later in the project life cycle, when it’s more expensive and difficult to make changes. 03:16 Nikita: So, the idea was to try to get it wrong sooner. Joe: That’s right, Niki. We wanted to get our analysis and designs in front of the customer as soon as possible to find out what was wrong with our models and then change the code as early in the life cycle as possible where it was both easier and, more importantly, cheaper to make changes before solidifying it in code. Of course, the key words here are “used correctly,” right? I saw the tools misused many times by those who weren’t trained properly or, more typically, by those whose software development methodology, if there even was one, didn’t use the tools properly—and of course the tools took the blame. CASE tools at the time held a lot of promise, but one could say vendors were overpromising and under delivering, although I did have a number of clients who were successful with them and could get useful support for their software development life cycle from the use of the tools. Since then, I’ve been very interested in using tools to make it easier for us to build software. 04:09 Nikita: So, let me ask you Joe, what is your definition of a CASE tool?  Joe: I’m glad you asked, Niki, because I think many people have many different definitions. I’m precise about it, and maybe even a bit pedantic with the definition. The definition I use for a CASE tool comprises four things. One, it uses graphics, graphical symbols, and diagrams to represent requirements and business rules for the application. Two, there is a repository, either private, or shared, or both, of models, definitions, objects, requirements, rules, diagrams, and other assets that can be shared, reused, and almost more importantly, tracked. Three, there’s a rule-base that prevents you from drawing things that can’t be implemented. For example, Visio was widely regarded as a CASE tool, but it really wasn’t because it had no rules behind it. You could wire together anything you wanted, but that didn’t mean it could be built. Fourth, it generates useful code, and it should do two-way engineering, where code, typically code changed outside the model, can be reverse engineered back into the model and apply updates to the model, and to keep the model and the source code in synchronization. 05:13 Joe: I came up with a good slogan for CASE tools years ago: a good CASE tool should automate the tedious, manual portions of software development. I’d add that one also needs to be smarter than the tools they’re using. Which reminds me, interestingly enough, of clients who would pick up CASE tools, thinking that they would make their software development life cycle shorter. But if they weren’t already building models for analysis or design, then automating the building of things that they weren’t building already was not going to save them time and effort. And some people adopted CASE tools because they were told to or worse, forced to, or they read an article on an airplane, or had a Eureka moment, and they would try to get their entire software development staff to use this new tool, overnight literally, in some cases. Absolutely sheer madness! Tools like this need to be brought into the enterprise in a slow, measured fashion with a pilot project and build upon small successes until people start demanding to use the tools in their own projects once they see the value. And each group, each team would use the CASE tool differently and to a different degree. One size most definitely does not fit all and identifying what the teams’ needs are and how the tool can automate and support those needs is an important aspect of adopting a CASE tool. It’s funny, almost everyone would agree there’s value in creating models and, eventually, generating code from them to get better systems and it should be faster and cheaper, etc. But CASE tools never really penetrated the market more than maybe about 18 to 25%, tops. 06:39 Lois: Huh, why? Why do you think CASE tools were not widely accepted and used? Joe: Well, I don’t think it was an issue with the tools so much as it was with a company’s software development life cycle, and the culture and politics in the company. And I imagine you’re shocked to hear that. Ideally, switching to or adopting automated tools like CASE tools would reduce development time and costs, and improve quality. So it should increase reusability too. But increasing the reusability of code elements and software assets is difficult and requires discipline, commitment, and investment. Also, there can be a significant amount of training required to teach developers, analysts, project managers, and senior managers how to deal with these different forms of life cycles and artifacts: how they get created, how to manage them, and how to use them. When you have project managers or senior managers asking where’s the code and you try telling them, “Well, it’s gonna take a little while. We’re building models and will press the button to generate the code.” That’s tough. And that’s also another myth. It was never a matter of build all the models, press the button, generate all the code, and be done. It’s a very iterative process. 07:40 Joe: I’ve also found that developers find it very psychologically reinforcing to type code into the keyboard, see it appear on the screen, see it execute, and models were not quite as satisfying in the same way. There was kind of a disconnect. And are still not today. Coders like to code. So using CASE tools and the discipline that went along with them often created issues for customers because it could shine a bright light on the, well let’s say, less positive aspects of their existing software development process. And what was seen often wasn’t pretty. I had several clients who stopped using CASE tools because it made their poor development process highly visible and harder to ignore. It was actually easier for them to abandon the CASE tools and the benefits of CASE tools than to change their internal processes and culture. CASE tools require discipline, planning, preparation, and thoughtful approaches, and some places just couldn’t or wouldn’t do that. Now, for those who did have discipline and good software development practices, CASE tools helped them quite a bit—by creating documentation and automating the niggly little manual tasks they were spending a lot of time on. 08:43 Nikita: You’ve mentioned in the past that CASE tools are still around today, but we don’t call them that. Have they morphed into something else? And if so, what? Joe: Ah, so the term Computer Aided Software Engineering morphed into something more acceptable in the ‘90s as vendors overpromised and under-delivered, because many people still saw value and do today see value in creating models to help with understanding, and even automating some aspects of software code development. The term model-based development arose with the idea that you could build small models of what you want to develop and then use that to guide and help with manual code development. And frankly just not using the word CASE was a benefit. “Oh we’re not doing CASE tools, but we’ll still build pictures and do stuff.” So, it could be automated and generate useful code as well as documentation. And this was both easy to use and easier to manage, and I think the industry and the tools themselves were maturing. 09:35 Joe: So, model-based development took off and the idea of building a model to represent your understanding of the system became popular. And it was funny because people were saying that these were not CASE tools, this was something different, oh for sure, when of course it was pretty much the same thing: rule-based graphical modeling with a repository that created and read code—just named differently. And as I go through this, it reminds me of an interesting anecdote that’s given about US President Abraham Lincoln. He once asked someone, “If you call a dog’s tail a leg, how many legs does a dog have?” Now, while you’re thinking about that, I’ll go ahead and give you the correct answer. It’s four. You can call a dog’s tail anything you want, but it still has four legs. You can call your tools whatever you want, but you still have the idea of building graphical representations of requirements based on rules, and generating code and engineering in both directions. 10:29 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started. 10:58 Nikita: Welcome back! Joe, how did you come to Oracle and its CASE tools? Joe: I joined Oracle in 1992 teaching the Oracle CASE tool Designer. It was focused on structured analysis and design, and could generate database Data Definition Language (DDL) for creating databases. And it was quite good at it and could reverse engineer databases as well. And it could generate Oracle Forms and Reports – character mode at first, and then GUI. But it was in the early days of the tool and there was definitely room for improvement, or as we would say opportunities for enhancement, and it could be hard to learn and work with. It didn’t do round-trip engineering of reading Oracle Forms code and updating the Designer models, though some of that came later. So now you had an issue where you could generate an application as a starting point, but then you had to go in and modify the code, and the code would get updated, but the models wouldn’t get updated and so little by little they’d go out of sync with the code, and it just became a big mess. But a lot of people saw that you could develop parts of the application and data definition in models and save time, and that led to what we call model-based development, where we use models for some aspects but not all. We use models where it makes sense and hand code where we need to code. 12:04 Lois: Right, so the two can coexist. Joe, how have model-based development tools been used at Oracle? Are they still in use? Joe: Absolutely! And I’ll start with my favorite CASE tool at Oracle, uhm excuse me, model-based development tool. Oracle SOA Suite is my idea of a what a model-based development tool should be. We create graphical diagrams to represent the flow of messages and message processing in web services—both SOAP and REST interfaces—with logic handled by other diagrammers and models. We have models for logic, human interaction, and rules processing. All this is captured in XML metadata and displayed as nice, colored diagrams that are converted to source code once deployed to the server. The reason I like it so much is Oracle SOA Suite addressed a fundamental problem and weakness in using modeling tools that generated code. It doesn’t let the developer touch the generated code. I worked with many different CASE tools over the years, and they all suffered from a fundamental flaw. Analysts and developers would create the models, generate the code, eventually put it into production, and then, if there was a bug in the code, the developer would fix the code rather than change the model. For example, if a bug was found at 10:30 at night, people would get dragged out of bed to come down and fix things. What they should have done is update the model and then generate the new code. But late at night or in a crunch, who’s going to do that, right? They would fix the code and say they’d go back and update the model tomorrow. But as we know, tomorrow never comes, and so little by little, the model goes out of synchronization with the actual source code, and eventually people just stopped doing models. 13:33 Joe: And this just happened more and more until the use of CASE tools started diminishing—why would I build a model and have to maintain it to just maintain the code? Why do two separate things? Time is too valuable. So, the problem of creating models and generating code, and then maintaining the code and not the model was a problem in the industry. And I think it certainly hurt the adoption and progress of CASE tool adoption. This is one of the reasons why Oracle SOA Suite is my favorite CASE tool…because you never have access to the actual generated code. You are forced to change the model to get the new code deployed. Period. Problem solved. Well, SOA Suite does allow post- deployment changes, of course, and that can introduce consistency issues and while they’re easier to handle, we still have them! So even there, there’s an issue. 14:15 Nikita: How and where are modeling tools used in current Oracle software development applications? Joe: While the use of CASE tools and even the name CASE fell out of favor in the early to mid-90s, the idea of using graphical diagrams to capture requirements and generate useful code does live on through to today. If you know what to look for, you can see elements of model-based design throughout all the Oracle tools. Oracle tools successfully use diagrams, rules, and code generation, but only in certain areas where it clearly makes sense and in well-defined boundaries. Let’s start with the software development environment that I work with most often, which is Visual Builder Studio. Its design environment uses a modeling tool to model relationships between Business Objects, which is customer data that can have parent-child relationships, and represent and store customer data in tables. It uses a form of entity relationship diagram with cardinality – meaning how many of these are related to how many of those – to model parent-child relationships, including processing requirements like deleting children if a parent is deleted. The Business Object diagrammer displays your business objects and their relationships, and even lets you create new relationships, modify the business objects, and even create new business objects. You can do all your work in the diagram and the correct code is generated. And you can display the diagram for the code that you created by hand. And the two stay in sync. There’s also a diagramming tool to design the page and page flow navigation between the pages in the web application itself. You can work in code or you can work in the diagram (either one or both), and both are updated at the same time. Visual Builder Studio uses a lot of two-way design and engineering. 15:48 Joe: Visual Builder Studio Page Designer allows you to work in code if you want to write HTML, JavaScript, and JSON code, or work in Design mode and drag and drop components onto the page designer canvas, set properties, and both update each other. It’s very well done. Very well integrated. Now, oddly enough, even though I am a model-based developer, I find I do most of my work in Visual Builder Studio Designer in the text-based interface because it’s so easy to use. I use the diagrammers to document and share my work, and communicate with other team members and customers. While I think it’s not being used quite so much anymore, Oracle’s JDeveloper and application development framework, ADF, includes built-in tools for doing Unified Modeling... /episode/index/show/oracleuniversitypodcast/id/30553063

Developing Redwood Applications 04/02/2024

Developing Redwood Applications Redwood is a state-of-the-art graphical interface that defines the look and feel of the new Oracle Cloud Redwood Applications. In this episode, hosts Lois Houston and Nikita Abraham, along with Senior Principal OCI Instructor Joe Greenwald, take a closer look at the intent behind the design and development aspects of the new Redwood experience. They also explore Redwood page templates and components. Developing Redwood Applications with Visual Builder: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! Last week, we discussed how Visual Builder Studio can be used to extend Oracle Fusion Apps. Lois: That’s right, Niki. In today’s episode, we will focus on Oracle’s Redwood design system and how it helps us create stunning, world-class enterprise applications and user experiences. 00:56 Nikita: Yeah, Redwood is the basis for all the new Oracle Cloud Applications being re-designed, developed, and delivered. To tell us more, we have Senior OCI Learning Solutions Architect and Principal Instructor Joe Greenwald, who’s been working with Oracle software development tools since the early 90s and is responsible for OU’s Visual Builder Studio and Redwood course content. Lois: Hi Joe! Thanks for being with us today. 01:21 Joe: Hi Lois. Hi Niki. I am excited to join you on this episode because with the release of 24A Fusion applications, we are encouraging all our customers to adopt the new Redwood design system and components, and take advantage of the world-class look and feel of the new Redwood user experience. Redwood represents a new approach and direction for us at Oracle, and we’re excited to have our customers benefit from it. 01:44 Nikita: Joe, you’ve been working with Oracle user interface development tools and frameworks for a long time. How and why is Redwood different? Joe: I joined Oracle in 1992, and the first Oracle user interface I experienced was Oracle Forms. And that was the character mode. I came from a background of Smalltalk and its amazing, pioneering graphical user interface (GUI) design capabilities. I worked at Apple and I developed my own GUIs for a few years on PCs and Macs. So, Character Mode Forms, what we used to call DMV (Department of Motor Vehicles) screens, was a shock, to say the least. Since then, I’ve worked with almost every user interface and development platform Oracle has created: Character Mode Forms, GUI Forms, Power Objects, HyperCard on the Macintosh, that was pre-OS X by the way, Sedona, written in native C++ and ActiveX and OLE, which didn’t make it to a product but appeared in other things later, ADF Faces, which uses Java to generate HTML pages, and APEX, which uses PL/SQL to generate HTML pages. And I’ve worked with and wrote training classes for Java Swing, an excellent GUI framework for event-driven desktop and enterprise applications, but it wasn’t designed for the web. So, it’s with pleasure that I introduce you to the Redwood design system, easily the best effort I’ve ever seen, from the look and feel of holistic user-goal-centered design philosophy and approach to the cutting-edge WYSIWYG design tools. 03:11 Lois: Joe, is Redwood just another set of styles, colors, and fonts, albeit very nice-looking ones? Joe: The Redwood platform is new for Oracle, and it represents a significant change, not just in the look and feel, colors, fonts, and styles, I mean that too, but it’s also a fundamental change in how Oracle is creating, designing, and imagining user interfaces. As you may be aware, all Oracle Cloud Applications are being re-designed, re-engineered, and re-rebuilt from the ground up, with significant changes to both back-end and front-end architectures. The front end is being redesigned, re-developed, and re-created in pure HTML5, CSS3, and JavaScript using Visual Builder Studio and its design-time browser-based Integrated Development Environment. The back end is being re-architected, re-designed, and implemented in a modern microservice architecture for Oracle Cloud using Kubernetes and other modern technologies that improve performance and work better in the cloud than our current legacy architecture. The new Oracle Cloud Applications platform uses Redwood for its design system—its tools, its patterns, its components, and page templates. Redwood is a richer and more productive platform to create solutions while still being cost-effective for Oracle. It encourages a transformation of the fundamental user experience, emphasizing identifying, meeting, and understanding end users’ goals and how the applications are used. 04:34 Nikita: Joe, do you think Oracle's user interface has been improved with Redwood? In what ways has the UI changed? Joe: Yes, absolutely. Redwood has changed a lot of things. When I joined Oracle back in the '90s, there was effectively no user interface division or UI team. There was no user interface lab—and that was started in the mid-‘90s—and I was asked to give product usability feedback and participate in UI tests and experiments in those labs. I also helped test the products I was teaching at the time. I actually distinctly remember having to take a week to train users on Oracle’s Designer CASE tool product just to prep the participants enough to perform usability testing. I can still hear the UI lab manager shaking her head and saying any product that requires a week of training to do usability testing has usability issues! And if you’re like me and you’ve been around Oracle long enough, you know that Oracle’s not always been known for its user interfaces and been known to release products that look like they were designed by two or more different companies. All that has changed with Redwood. With Redwood, there’s a new internal design group that oversees the design choices of all development teams that develop products. This includes a design system review and an ongoing audit process to ensure that all the products being released, whether Fusion apps or something else, all look and feel similar so it looks like it’s designed by a single company with a single thought in mind. Which it is. 06:00 Joe: There’s a deeper, consistent commitment in identifying user needs, understanding how the applications are being used, and how they meet those user needs through things like telemetry: gathering metrics from the actual components and the Redwood system itself to see how the applications are being used, what’s working well, and what isn’t. This telemetry is available to us here at Oracle, and we use it to fine tune the applications’ usability and purpose. 06:25 Lois: That’s really interesting, Joe. So, it’s a fundamental change in the way we’re doing things. What about the GUI components themselves? Are these more sophisticated than simple GUI components like buttons and text fields? Joe: The graphical components themselves are at a much higher level, more comprehensive, and work better together. And in Redwood, everything is a component. And I’m not just talking about things like input text fields and buttons, though it applies to these more fine-grained components as well. Leveraging Oracle’s deep experience in building enterprise applications, we’ve incorporated that knowledge into creating page templates so that the structure and look and feel of the page is fixed based on our internal design standards. The developer has control over certain portions of it, but the overall look and feel of the page is controlled by Oracle. So there is consistency of look and feel within and across applications. These page templates come with predefined functionalities: headers, titles, properties, and variables to manipulate content and settings, slots for other components to hold like search fields, collections, contextual information, badges, and images, as well as primary and secondary actions, and variables for events and event handling through Visual Builder action chains, which handle the various actions and processing of the request on the page. And all these page templates and components are responsive, meaning they respond to the change in the size of the page and the orientation. So, when you move from a desktop to a handheld mobile device or a tablet, they respond appropriately and consistently to deliver a clean, easy-to-use interface and experience. 07:58 Nikita: You mentioned WYSIWYG design tools and their integration with Visual Builder Studio’s integrated development environment. How does Redwood work with Visual Builder Studio? Joe: This is easily one of my most favorite aspects about Redwood and the integration with Visual Builder Studio Designer. The components and page templates are responsive at runtime as well as responsive at design time! In over 30 years of working with Oracle software development products, this is the first development system and integrated development environment I’ve seen Oracle produce where what you see is what you get at design time. Now, with products such as Designer and JDeveloper ADF Faces and even APEX—all those page-generation types of products—you have to generate the page, deploy it, and only then can you view the final page to see whether it meets the needs of your user interface. For example, with Designer, there were literally hundreds of configuration parameters that you could set to control how forms and reports looked when they were generated —down to how many buttons on a row or how many rows to a page, that sort of thing, all done in text mode. Then you’d generate and run the page to see what the result was and then go back and modify things until you got what you wanted. 09:05 Joe: I remember hearing the product managers for Oracle ADF Faces being asked…well, a customer asked, “What happens if I put this component here and this component here? What will the page look like?” and they’d say, “I don’t know. Render the page and let’s see.” That’s just crazy talk. With Redwood and its integration with Visual Builder Studio Designer, what you see on the page at design time is literally what you get. And if I make the page narrower or I even convert it to a mobile display while in the Designer itself, I immediately see what the page looks like in that new mode. Everything just moves accordingly, at design time. For example, when changing to a mobile UI, everything stacks up nicely; the components adjust to the page size and change right there in the design environment. Again, I can’t emphasize enough the simple luxury of being able to see exactly what the user is going to see on my page and having the ability to change the resolution, orientation, and screen size, and it changes right there immediately in my design environment. 10:01 Lois: I’m intrigued by the idea of page templates that are managed by Oracle but still leave room for the developer to customize aspects of the look and feel and functionality. How does that work? Joe: Well, the page templates themselves represent the typical pages you would most likely use in an enterprise application. Things like a welcome page, a search page, and edit and create pages, and a couple of different ways to display summary information, including foldout pages, though this is not an exhaustive list of course. Not only do they provide a logical and complete starting point for the layout of the page itself, but they also include built-in functionality. These templates include functionality for buttons, primary and secondary actions, and areas for holding contextual information, badges, avatars, and images. And this is all built right into the page, and all of them use variables to describe the contents for the various parts, so the contents can change programmatically as the variables’ contents change, if necessary. 10:59 Do you have an idea for a new course or learning opportunity? We’d love to hear it! Visit the Oracle University Learning Community and share your thoughts with us on the Idea Incubator. Your suggestion could find a place in future development projects. Visit mylearn.oracle.com to get started. 11:19 Nikita: Welcome back! So, Joe, let’s say I’m a developer. How do I get started working with Redwood? Joe: One of the easiest ways to do it is to use Visual Builder Studio Designer and create a new visual application. If you’re creating a standalone, bespoke custom application, you can choose a Redwood starter template, which will include all the Redwood components and page templates automatically. Or, if you’re extending and customizing an Oracle Fusion application, Redwood is already included. Either way, when you create a new page, you have a choice of different page templates—welcome page templates, edit pages, search pages, etc. —and all you have to do is choose a page that you want and begin configuring it. And actually if you make a mistake, it’s easy to switch page templates. All the components, page templates, and so on have documentation right there inside Visual Builder Studio Designer, and we do recommend that you read through the documentation first to get an understanding of what the use case for that template is and how to use it. And some components are more granular, like a collection container which holds a collection of rows of a list or a table and provides capabilities like toolbars and other actions that are already built and defined. You decide what actions you want and then use predefined event listeners that are triggered when an event occurs in the application—like a button being clicked or a row being selected—which kicks off a series of actions to be performed. 12:38 Lois: That sounds easy enough if you know what you’re doing. Joe, what are some of the more common pages and what are they used for? Joe: Redwood page templates can be broken down into categories. There are overview templates like the welcome page template, which has a nice banner, colors, and illustrations that can be used for a welcoming page—like for entering a new application or a new logical section of the application. The dashboard landing page template displays key information values and their charts and graphs, which can come from Oracle Analytics, and automatically switches the display depending on which set of data is selected. The detail templates include a general overview, which presents read-only information related to a single record or resource. The item overview gives you a small panel to view summary information (for example, information on a customer) and in the main section, you can view details like all the orders for that customer. And you can even navigate through a set of customers, clicking arrows for next-previous navigation. And that’s all built in. There’s no programming required. The fold-out page template folds out horizontally to show you individual panels with more detail that can be displayed about the subject being retrieved as well as overflow and drill-down areas. And there’s a collection detail template that will display a list with additional details about the selected item (for example, an order and its order line items). 13:52 Joe: The smart search page does exactly what it says. It has a search component that you use to filter or search the data coming back from the REST data sources and then display the results in a list or a table. You define the filter yourself and apply it using different kinds of comparators, so you can look for strings that start with certain values or contain values, or numerical values that are equal to or less than, depending on what you’re filtering for. And then there are the transactional templates, which are meant to make changes. This includes both the simple create and edit and advanced create and edit templates. The simple create and edit page template edits a single record or creates a single record. And the advanced page template works well if you’re working with master-detail, parent-child type relationships. Let’s say you want to view the parent and create children for it or even create a parent and the children at the same time. And there’s a Gantt chart page for project management–type tracking and a guided process page for multiple-step processes and there’s a data management page template specifically for viewing and editing data collections like Excel spreadsheets. 14:50 Nikita: You mentioned that there’s a design system behind all this. How is this used, and how does the customer benefit from it? Joe: Redwood comprises both a design system and a development system. The design system has a series of steps that we follow here at Oracle and can suggest that you, our customers and partners, can follow as well. This includes understanding the problem, articulating the vision for the page and the application (what it should do), identifying the proper Redwood page templates to use, adding detail and refining the design and then using a number of different mechanisms, including PowerPoint or Figma design tools to specify the design for development, and then monitor engagement in the real world. These are the steps that we follow here at Oracle. The Redwood development process starts with learning how to use Redwood components and templates using the documentation and other content from redwood.oracle.com and Visual Builder Studio. Then it’s about understanding the design created by the design team, learning more about components and templates for your application, specifically the ones you’re going to use, how they work, and how they work together. Then developing your application using Visual Builder Studio Designer, and finally improving and refining your application. Now, right now, as I mentioned, telemetry is available to us here at Oracle so we can get a sense of the feedback on the pages of how components are being used and where time is being spent, and we use that to tune the designs and components being used. That telemetry data may be available to customers in the future. Now, when you go to redwood.oracle.com, you can access the Redwood pattern book that shows you in detail all the different page templates that are available: smart search page, data grid, welcome page, dashboard landing page, and so on, and you can select these and read more about them as well as the actual design specifications that were used to build the pages—defining what they do and what they respond to. They provide a lot of detailed information about the templates and components, how they work and how they’re intended to be used. 16:45 Lois: That’s a lot of great resources available. But what if I don’t have access to Visual Builder Studio Designer? Can I still see how Redwood looks and behaves? Joe: Well, if you go to redwood.oracle.com, you can log in and work with the Redwood reference application, which is a live application working with live data. It was created to show off the various page templates and components, their look and feel and functionality from the Redwood design and development systems. This is an order management application, so you can do things like view filtered pending orders, create new orders, manage orders, and view information about customers and inventory. It uses the different page... /episode/index/show/oracleuniversitypodcast/id/30550688

Preparing to Extend Oracle Fusion Apps Using Visual Builder Studio 03/26/2024

Preparing to Extend Oracle Fusion Apps Using Visual Builder Studio What do you need to start customizing the next generation of Oracle Fusion Apps? How do you create new pages for business processes? What level of expertise do you require for this? Join Lois Houston and Nikita Abraham as they get answers to all these questions and more from Senior Principal OCI Instructor Joe Greenwald. Develop Fusion Applications Using Visual Builder Studio: Build Visual Applications Using Visual Builder Studio: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! Last week, we were introduced to Visual Builder Studio and the Oracle JavaScript Extension Toolkit, also known as JET. Lois: Our friend and Senior Principal OCI Instructor Joe Greenwald is back with us today to talk about how to extend Oracle Cloud Applications that are being built using Visual Builder for its front-end. Nikita: That’s right. All Fusion Applications are being redesigned and rebuilt using Visual Builder. And we’ll find out more about that from Joe. Hi Joe! Thanks for being with us today. Joe: Hi Lois! Hi Niki! My pleasure to be here. 01:09 Nikita: Joe, tell us a little about what’s happening with the redesign and re-architecture of Oracle Cloud Applications using Visual Builder Studio, or VBS. I hear some very exciting changes are coming that are important for our customers and partners. Joe: That’s right, Niki. Oracle is redesigning and rebuilding its entire suite of Fusion Cloud Applications, over 330 different products, utilizing over 60,000 engineers — that is “60,” not “16” — at Oracle to develop the next generation of Oracle Fusion Applications. What’s most exciting is that the same tools the engineers are using to accomplish this are available to our partners and our customers to use to extend the functionality and capabilities of Fusion Applications to meet their custom needs and processes. 01:54 Lois: That’s pretty awesome! We want to use this time today to ask you about extensions, the types of extensions you can create, and how to use Visual Builder Studio to create those extensions. Nikita: Yeah, can we start with you telling us what an extension is? I’ve gotten the sense that Oracle uses the term extension as both a noun and a verb and that’s a bit confusing to me. 02:15 Joe: Yeah, good catch, Niki. Yes, Oracle does use the term extension in two ways: both as a noun and a verb. As a noun, an extension is a container for the code changes that you make to your applications. Basically, it’s a Git repository that Oracle creates and manages for you. So, the extension container holds the code changes you make to your page layouts: the fields, their positioning, showing and hiding fields, that sort of thing, as well as page functionality. These code changes you make are stored in the extension and it is this extension with your code changes that is merged with the main Git branch eventually and then deployed using continuous integration/continuous deployment jobs defined in Visual Builder Studio, which manages the project and its assets. Your extension is a Git branch that is an asset of the project. Once your extension code is merged with the main branch and deployed, then the next time someone brings up the application, they’ll see the changes you’ve made in the app. 03:08 Lois: And as a verb? Joe: As a verb, extension means to extend the functionality and the look and feel of the application, though I prefer the term customization or configuration to describe this aspect, as the documentation does, and to avoid confusion, though I’ll admit I’m not always consistent about the terms I use. 03:26 Lois: What types of customizations, or extensions, and I’m using the verb now, are available for Fusion Apps in Visual Builder Studio? Joe: There are three different ways Fusion Apps can be customized effectively, configured, or extended. The first way is what we call a basic extension, where you’re rearranging hiding, or showing, or moving around fields and sections on the page that have been set up to be extendable by the Fusion Application development teams. Things like hiding fields, showing fields, hiding sections, showing sections… Nikita: So fairly basic actions… Joe: Yeah exactly and they can be done in Visual Builder Studio Designer by people with minimal VB training, Visual Builder training. And, most recently, if you have access to it, you can do it in the new Express mode, where the page shows you just those things you can work with and just the tools you need to work with the page. This is new and makes it much easier for folks who are not highly technical to make basic changes to the page layout. 04:18 Lois: People like me! That sounds easy enough. Joe: And the next type of extension is more of an intermediate change and requires some training with Visual Builder Studio because you’re creating rules that govern the display of layouts based on certain conditions on the page. These are highly flexible, powerful, and useful for creating customized page layouts based on a variety of factors from page size and orientation to the role of the person using it to values in the actual fields on the page itself. These rules can be combined to create complex rule-based conditions that display exactly what the user should see, given the conditions of the page and their role. I would also include making changes to action chains, which execute sequences of behaviors and navigation, and the actual structure of the application, but this is more advanced. Lastly, is creating mashup applications, which are stand-alone Visual Builder visual applications, which use data from Fusion apps, and customer data sources, like their own database tables, and potentially third-party APIs to create brand new pages and applications with new functionality, new processes, new procedures, new displays, all of which look just like Fusion Applications and use the same data as Fusion applications. 05:27 Lois: Joe, how do I get started if I want to extend a page? Joe: The easiest way to do it is to open a page in Fusion Applications and then select Edit Page in Visual Builder Studio from the Profile menu. You’re then prompted for a project to hold the Git repository for the extension container. And since there’s probably already one that exists, after you select the project, an extension Git container is assigned to you. Unless this is the very first time the application has been extended in which case it creates an extension for you. When creating customizations or configurations, we recommend that each application be done in its own separate project. So, for example, if you’re working on Customer Experience Sales, you might do it in Project A and if you’re working on extensions with HCM, you might do it in Project B. And if you decide to create your own pages and flows in your own app, you might do that in Project C. 06:13 Nikita: But why do you need to do this? Joe: That’s just to keep things nice and separate and organized. The tool, Visual Builder Studio, doesn’t really care, but it makes for cleaner development and can help with the management of the development teams. 06:23 Nikita: Ok, Joe, I have a question. How do I know if the page I’m on in Fusion Apps can be edited in Visual Builder? I know there are a lot of legacy pages still out there and they can co-exist with the new VB-based pages. Joe: If the URL of the page you’re on has the word /Redwood in it instead of /faces, then you know this is a page that was created using Visual Builder Studio and you’ll be able to extend it and make changes to it using the Edit in Visual Builder Studio option. So, if you select Edit in Visual Builder Studio, then the page you are on opens inside Visual Builder Studio Designer and you can make changes to any part of the page that has been explicitly enabled for extension by the development team. 07:02 Lois: That’s an important part, right? The application is not extendable by default. Joe: That’s right, Lois. It is all locked down and you can’t make any changes to it by default. The development team must specifically enable certain parts of the page: sections, fields, layouts, variables, types, action chains, etc. as extendable for you to be able to make changes to it. This ensures the changes the development team makes to the application in the future won’t break your extensions. And conversely, the development team can choose to not extend portions that they do not want you to touch or mess with. Then if they do change that bit of the app in the future, it won’t break the application and you won’t get a big surprise. So, using the Edit page in Visual Builder Studio, you can make both basic changes, like moving, showing, and hiding fields and sections, as well as the more intermediate types of configurations, like using dynamic components to create rule-based layouts that change dynamically based on several conditions such as page size, roles of the user, and field values on the page itself. 08:00 Nikita: What happens if two developers make changes and essentially overwrite each other’s customizations — say one hides a field and another later exposes it? Joe: Well, whoever commits their changes and deploys last wins. The other developer’s changes get overwritten. So, this is something the team would want to consider carefully. It is possible to roll back to an earlier version if one must. And this can be done in Visual Builder Studio — the part that manages project assets like Git repositories. And there are Oracle blog posts about how to do that if you’re interested in learning more. 08:29 Lois: Joe, earlier you mentioned creating new pages and flows, but so far you’ve only talked about modifying existing extendable pages. How do I create new pages and flows? Joe: In a Visual Builder extension, a set of pages and flows is called an App UI. When I use the terms pages and flows, what I’m talking about is a set of pages that are logically related—whatever logical means to the designer and developer—in a group called a flow that you can navigate between. But you can also navigate between flows and even between applications. So, without getting too technical, each application has a default flow, which has a default page where that flow starts when the app first comes up. So, you can think of an App UI as a collection of flows and their pages, and a URL that accesses the default flow and its default page. That’s the page you would see first when accessing that URL. Of course, this can be configured and changed by the developer, as needed. Now, when Oracle creates the original application (for example, digital sales, helpdesk, or something like that), we create an App UI, which contains the pages and flows for that application and is the “entry point” into the app, accessing that App UI’s default flow and its default page and then things flow on from there. 09:40 Joe: Partners and customers can create their own application extensions that are dependent on an Oracle application and even create their own App UI – their own sets of pages and flows to accommodate their own processing and workflow needs. This gives them the ability to add their own processes and rules, and still leverage and navigate to the core application that Oracle built. For example, say Oracle delivered digital sales as an Oracle Cloud Application built using Visual Builder to a customer and the customer needs to add a few pages to do some validation or other type of business processing before entering the digital sales application. What the customer does, in this case, is create a new extension of the Oracle Digital Sales app and an App UI of their own, which would be the set of pages and flows that contain the processing they want to start with before then navigating into the digital sales app to use Oracle’s application. 10:31 Nikita: Wait, did I hear that correctly? We’re creating an extension of an extension or creating an extension on an existing extension? Joe: I know, right? I realize this can sound confusing the first time you hear it or the second time or even the third time. It took me a while to get my head around what they’re talking about. Let’s start with a Fusion application. In a Fusion application, everything is an extension of something. This is just how the code base and the architecture are organized and how they manage the Git repositories and the code base itself. So, Oracle created a base application called the Unified App. The Unified Application contains the basic page structure and common functionality needed for all applications. For example, it contains the header at the top that has the profile and the footer at the bottom of the page that has that little Ask Oracle icon. 11:16 Joe: Within that page, between the header and the footer, are the pages that are created by the developers, whether they be Oracle engineers or partners or customers. They display the contents of the page with the data and the layouts and all of that. In a sense, you can think of the Unified App as an index page, the starting page of the web application. Though that’s not completely true technically, it’s good enough for illustrative purposes. So, Oracle starts with the Unified App and then a development team extends that Unified App to build their product. This is how digital sales did it. This is how customer experience did it. This is how helpdesk did it. They start with the Unified App and they extend that and create an App UI that contains the flows and pages for their specific application, and then add functionality for all the pages and flows, as needed for the design. Partners and customers can then create a new extension that extends the Oracle Application and add their own App UI and their own URL if they want their pages accessed first, before navigating to the Oracle application. For example, if the digital sales application has functionality you’d like to leverage, like it has data services or fragments or page layouts that you want to reuse or other things, you extend the digital sales application, and this extension holds your code changes. You could then create a new App UI, and once deployed, users can use that URL for the new App UI to access your new pages. And your page can then navigate to the Oracle app when it needs to. Though I will say to date, we’re really not seeing much demand for this particular use case, but it is possible. 12:42 Lois: Is that the only option available to customers and partners—to extend an existing Oracle application? Joe: No, Lois. We’re seeing customers and partners create brand new Fusion applications of their own, based on the Unified App Oracle created. In a sense, doing the same thing that our development teams here are doing. Remember, I said an Oracle development team starts with the Unified App, which has common functionality and look and feel for all applications, and then extends that to add business rules processing, flows, App UI, whatever they need for their specific Oracle application. We’re seeing our partners and customers wanting to build their own applications. Maybe a customer or partner wants to create a Time & Expense application and leverage the Fusion application data and the APIs available, but define their own flows, their own pages, their own processing. This is very easy to do. They’d start by extending the Unified App just like the Oracle development teams do, and then build their own App UI and within that, their own flows, pages, and custom processing. The nice thing about it is that the application looks and works and feels just like a Fusion application and it appears alongside other Fusion applications, because it is a Fusion application. 13:52 Did you know that the Oracle University Learning Community regularly holds live events hosted by Oracle expert instructors. Find out how to prepare for your certification exams. Learn about the latest technology advances and features. Ask questions in real time and learn from an Oracle subject matter expert. From Ask Me Anything about certification to Ask the Instructor coaching sessions, you’ll be able to achieve your learning goals for 2024 in no time. Join a live event today and witness firsthand the transformative power of the Oracle University Learning Community. Visit mylearn.oracle.com to get started. 14:33 Nikita: Welcome back! So Joe, it sounds like there are two different paths or life cycles to create extensions for future applications in Visual Builder Studio. Is that correct? Joe: Yes, exactly. So one path to extending the functionality of Fusion apps is to edit the page in Visual Builder Studio, which opens the page in Visual Builder Designer, and you then make changes to the existing pages, depending on what the development team has made extendable. 14:58 Nikita: But you can’t create new pages and flows in this scenario, right? Joe: This is strictly about modifying an existing page. The other path is creating a new application extension, which is a new application from scratch or extending an existing Oracle application or even an existing partner or customer application. Again, we’re not seeing this typically being done too much. Most partners and customers create new applications or make customizations to existing pages. But the architecture does support it. So, your partner might create a new application based on the production app released by Oracle, and you could extend their application. Or a development team at your site could extend Oracle’s application and you could then extend that team’s application. This is mechanically possible, although I question the use case behind that. Usually, we see our apps being extended – becoming a dependency when there’s code that can be leveraged or reused for a new app and its new App UI. 15:49 Lois: Joe, what did you mean when you say one extension is a dependency of another? Can you talk a bit about dependencies, what that means, how it looks to the developer? Joe: When you extend an application, it becomes a dependency to your application, and you get access to all the resources within that dependency that are marked as extendable by the developer who created that extension. Most useful are things like service connections to REST APIs from Fusion apps data sources, reusable code fragments, and layouts that you can leverage in those cases where you want to create a new App UI. When an extension is listed as a dependency, you’ll see this graphically in Visual Builder Studio Designer. When you see an extension listed as a dependency, it means you can reference any of that extension’s resources that have been marked extendable by the developer. Recall all resources are closed off or hidden by default, but development teams can mark resources as open to being extended and reused, and then you can see and use those resources. So, you can easily add and remove extensions as dependencies in Visual Builder Designer as needed. Now, this can be a nice way to modularize and reuse your resources and assets. To summarize: I can modify an existing page – this is most common, extend an existing application and create a new App UI – which is not common, or I... /episode/index/show/oracleuniversitypodcast/id/30394658

Introduction to Visual Builder Studio, Visual Builder Cloud Service, Stand-Alone, and JET 03/19/2024

Introduction to Visual Builder Studio, Visual Builder Cloud Service, Stand-Alone, and JET The next generation of front-end user interfaces for Oracle Fusion Applications is being built using Visual Builder Studio and Oracle JavaScript Extension Toolkit. However, many of the terms associated with these tools can be confusing. In this episode, Lois Houston and Nikita Abraham are joined by Senior Principal OCI Instructor Joe Greenwald. Together, they take you through the different terminologies, how they relate to each other, and how they can be used to deliver the new Oracle Fusion Applications as well as stand-alone, bespoke visual web applications. Develop Fusion Applications Using Visual Builder Studio: Build Visual Applications Using Visual Builder Studio: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. --------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! Today, we’re starting a new season on building the next generation of Oracle Cloud Apps with Visual Builder Studio. 00:45 Lois: And I’m so excited that we have someone really special to take us through the next few episodes. Joe Greenwald is joining us. Joe is a Senior Principal OCI Instructor with Oracle University. He joined Oracle in 1992 with an extensive background in CASE tools. Since then, he has used and taught all of Oracle's software development tools, including Oracle Forms, APEX, JDeveloper ADF, as well as all the Fusion Middleware courses. Currently, Joe is responsible for the Visual Builder Studio and Redwood development courses, including extending Fusion Applications with Visual Builder. 01:22 Nikita: In today’s episode, we’re going to ask Joe about Visual Builder Studio and Oracle JavaScript Extension Toolkit, also known as JET. Together, they form the basis of the technology for the next generation of front-end user interfaces for Oracle Fusion Applications, as well as many other Oracle applications, including most Oracle Cloud Infrastructure (OCI) interfaces. Lois: We’ll look at the different terminologies and technologies, how they relate to each other, and how they deliver the new Oracle Fusion applications and stand-alone, bespoke visual web applications. Hi Joe! Thanks for being with us today. 01:57 Joe: Hi Lois! Hi Niki! I’m glad to be here. Nikita: Joe, I’m somewhat thrown by the terminology around Visual Builder, Visual Studio, and JET. Can you help streamline that for us? Lois: Yeah, things that are named the same sometimes refer to different things, and sometimes things with a different name refer to the same thing. 02:15 Joe: Yeah, I know where you’re coming from. So, let’s start with Visual Builder Studio. It’s abbreviated as VBS and can go by a number of different names. Some of the most well-known ones are Visual Builder Studio, VBS, Visual Builder, Visual Builder Stand-Alone, and Visual Builder Cloud Service. Clearly, this can be very confusing. For the purposes of these episodes as well as the training courses I create, I use certain definitions. 02:39 Lois: Can you take us through those? Joe: Absolutely, Lois. Visual Builder Studio refers to a product that comes free with an OCI account and allows you to manage your project-related assets. This includes the project itself, which is a container for all of its assets. You can assign teams to your projects, as well as secure the project and declare roles for the different team members. You manage GIT repositories with full graphical and command-line GIT support, define package, build, and deploy jobs, and create and run continuous integration/continuous deployment graphical and code-managed pipelines for your applications. These can be visual applications, created using the Visual Builder Integrated Development Environment, the IDE, or non-visual apps, such as Java microservices, docker builds, NPM apps, and things like that. And you can define environments, which determine where your build jobs can be deployed. You can also define issues, which allow you to identify, track, and manage things like bugs, defects, and enhancements. And these can be tracked in code review merge requests and build jobs, and be mapped to agile sprints and scrum boards. There’s also support for wikis for team collaboration, code snippets, and the management of the repository and the project itself. So, VBS supports code reviews before code is merged into GIT branches for package, build, and deploy jobs using merge requests. 03:57 Nikita: OK, what exactly do you mean by that? Joe: Great. So, for example, you could have developers working in one GIT branch and when they’re done, they would push their private code changes into that remote branch. Then, they’d submit a merge request and their changes would be reviewed. Once the changes are approved, their code branch is merged into the main branch and then automatically runs a CI/CD package (continuous integration/continuous deployment) package, build, and deploy job on the code. Also, the CI/CD package, build, and deploy jobs can run against any branches, not just the main branch. So Visual Builder Studio is intended for managing the project and all of its assets. 04:37 Lois: So Joe, what are the different tools used in developing web applications? Joe: Well, Visual Builder, Visual Builder Studio Designer, Visual Builder Designer, Visual Builder Design-Time, Visual Builder Cloud Service, Visual Builder Stand-Alone all kind of get lumped together. You can kinda see why. What I’m referring to here are the tools that we use to build a visual web application composed of HTML5, CSS3, JavaScript, and JSON (JavaScript Object Notation) for metadata. I call this Visual Builder Designer. This is an Integrated Development Environment, it’s the “IDE” which runs in your browser. You use a combination of drag and drop, setting properties, and writing and modifying custom and generated code to develop your web applications. You work within a workspace, which is your own private copy of a remote Git branch. When you’re ready to start development work, you open an existing workspace or create a new one based on a clone of the remote branch you want to work on. Typically, a new branch would be created for the development work or you would join an existing branch. 05:35 Nikita: What’s a workspace, Joe? Is it like my personal laptop and drive? Joe: A workspace is your own private code area that stores any changes you make on the Oracle servers, so your code changes are never lost—even when working in a browser-based, network-based tool. A good analogy is, say I was working at home on my own machine. And I would make a copy of a remote GIT branch and then copy that code down to my local machine, make my code changes, do my testing, etc. and then commit my work—create a logical save point periodically—and then when I’m ready, I’d push that code up into the remote branch so it can be reviewed and merged with the main branch. My local machine is my workspace. However, since this code is hosted up by Oracle on our servers, and the code and the IDE are all running in your browser, the workspace is a simulation of a local work area on your own computer. So, the workspace is a hosted allocation of resources for you that’s private. Other people can’t see what’s going on in your workspace. Your workspace has a clone of the remote branch that you’re working with and the changes you make are isolated to your cloned code in your workspace. 06:38 Lois: Ok… the code is actually hosted on the server, so each time you make a change in the browser, the change is written back to the server? Is it possible that you might lose your edits if there’s a networking interruption? Joe: I want to emphasize that while I started out not personally being a fan of web-based integrated development environments, I have been using these tools for over three years and in all that time, while I have lost a connection at times—networks are still subject to interruptions—I’ve never lost any changes that I’ve made. Ever. 07:08 Nikita: Is there a way to save where you are in your work so that you could go back to it later if you need to? Joe: Yes, Niki, you’re asking about commits and savepoints, like in a Git repository or a Git branch. When you reach a logical stopping or development point in your work, you would create a commit or a savepoint. And when you’re ready, you would push that committed code in your workspace up to the remote branch where it can be reviewed and then eventually merged, usually with the main Git branch, and then continuous integration/continuous package and deployment build jobs are run. Now, I’m only giving you a high-level overview, but we cover all this and much more in detail with hands-on practices in our Visual Builder developer courses. Right now, I’m just trying to give you a sense of how these different tools are used. 07:49 Lois: Yes, that makes sense, Joe. It’s a lot to cover in a short amount of time. Now, we’ve discussed the Visual Builder Designer IDE and workspace. But can you tell us more about Visual Builder Cloud Service and stand-alone environments? What are they used for? What features do they provide? Are they the same or different things? Joe: Visual Builder Cloud Service or Visual Builder Stand-Alone, as it’s sometimes called, is a service that Oracle hosts on its servers. It provides hosting for the deployed web application source code as well as database tables for business objects that we build and maintain to store your customer data. This data can come from XLS or CSV files, or even your own Oracle database customer table data. A custom REST proxy makes calls to external third-party REST services on your behalf and supports several popular authentication mechanisms. There is also integration with the Identity Cloud Service (IDCS) to manage users and their access to your web apps. 08:47 Joe: Visual Builder Cloud Service is a for-fee product. You pay licensing fees for how much you use because it’s a hosted service. Visual Builder Studio, the project asset management aspect I discussed earlier, is free with a standard OCI license. Now, keep in mind these are separate from something like Visual Builder Design Time and the service that’s running in Fusion application environments. What I’m talking about now is creating standalone, bespoke, custom visual applications. These are applications that are built using industry-standard HTML5, CSS3, JavaScript, and JSON for metadata and are hosted on the Oracle servers. 09:27 Are you looking for practical use cases to help you plan and apply configurations that solve real-world challenges? With the new Applied Learning courses for Cloud Applications, you'll be able to practically apply the concepts learned in our implementation courses and work through case studies featuring key decisions and configurations encountered during a typical Oracle Cloud Applications implementation. Applied learning scenarios are currently available for General Ledger, Payables, Receivables, Accounting Hub, Global Human Resources, Talent Management, Inventory, and Procurement, with many more to come! Visit mylearn.oracle.com to get started. 10:09 Nikita: Welcome back! Joe, you said Visual Builder Cloud Service or Stand-Alone is a for-fee service. Is there a way I can learn about using Visual Builder Designer to build bespoke visual applications without a fee? Joe: Yes. Actually, we’ve added an option where you can run the Visual Builder Designer and learn how to create web apps without using the app hosting or the business object database that stores your customer data or the REST proxy for authentication or the Identity Cloud Service. So you don’t get those features, but you can still learn the fundamentals of developing with Visual Builder Designer. You can call third-party APIs, you can download the source, and run it locally, for example, in a Tomcat server. This is a great and free way to learn how to develop with the Visual Builder Designer. 10:52 Lois: Joe, I want to know more about the kinds of apps you can build in VB Designer and the capabilities that VB Cloud Service provides. Joe: Visual Builder Designer allows you to build custom, bespoke web applications made of interactive webpages; flows of pages for navigation; events that respond when things happen in the app, for example, GUI events like a button is clicked or values are entered into a text field; variables to store state and the ability to make REST calls, all from your browser. These applications have full access to the Oracle Fusion Applications APIs, given that you have the right security permissions and credentials of course. They can access your customer business data as business objects in our internally hosted database tables or your own customer database tables. They can access third-party APIs, and all these different data sources can appear in the same visual application, on the same page, at the same time. They use the identity cloud service to identify which users can log in and authenticate against the application. And they all use the new Redwood graphical user interface components and page templates, so they have the same look and feel of all Oracle applications. 11:59 Nikita: But what if you’re building or extending Oracle Fusion Applications? Don’t things change a little bit? Joe: Good point, Niki. Yes. While you still work within Visual Builder Studio, that doesn’t change, VBS maintains your project and all your project-related assets, that is still the same. However, in this case, there is no separate hosted Visual Builder Cloud Service or Stand-Alone instance. In this case, Visual Builder is hosted inside of Fusion apps itself as part of the installation. I won’t go into the details of how the architecture works, but the Visual Builder instance that you’re running your code against is part of Fusion applications and is included in the architecture as well as the billing. All your code changes are maintained and stored within a single container called an extension. And this extension is a Git repository that is created for you, or you can create it yourself, depending on how you choose to work within Visual Builder Studio. You create an extension to hold the source code changes that provide a customization or configuration. This means making a change to an existing page or a set of pages or even adding new pages and flows to your Oracle Fusion Applications. You use Visual Builder Studio and Visual Builder Designer in a similar way as to how you would use them for bespoke stand-alone visual applications. 13:10 Lois: I’m trying to envision how this workflow is used. How is it different from bespoke VB app development? Or is it different at all? Joe: So, recall that the Visual Builder Designer is effectively the Integrated Development Environment, the IDE, where you make your code changes by working with both the raw HTML5, CSS3, and JavaScript code, if need be, or the Page Designer for drag and drop, and setting properties and then Live mode to test your work. You use a version of VB Designer to view and modify your customizations, and the code is stored in a Git repository called an extension. So, in that sense, the work of developing pages and flows and such is the same. You still start by creating or, more typically, joining a project and then either create a new extension from scratch or base it on an existing application, or go directly to the page that you want to edit and, on that page, select from your profile menu to edit in Visual Builder Studio. Now, this is a different lifecycle path from bespoke visual applications. With them, you’re not extending an app or modifying individual pages in the same way. 14:11 Joe: You get a choice of which project you want to add your extension to when you’re working with Fusion apps and potentially which repository to store your customizations, unless one already exists and then it’s assigned automatically to hold your code changes. So you make your changes and edits to the portions of the application that have been opened for extensibility by the development team. This is another difference. Once you make your code changes, the workflow is pretty much the same as for a bespoke visual application: do your development work, commit your changes, push your changes to the remote branch. And then typically, your code is reviewed and if the code passes and is approved, it’s merged with the main branch. Then, the package and deploy jobs run to deploy the main code to the production environment or whatever environment you’re targeting. And once the package and deploy jobs complete, the code base is updated and users who log in see the changes that you’ve made. 15:00 Nikita: You mentioned creating apps that combine data from Fusion cloud, applications, customer data, and third-party APIs into one page. Why is it necessary? Why can’t you just do all that in one Fusion Applications extension? Joe: When you create extensions, you are working within the Oracle Fusion Applications ecosystem, that’s what they actually call it, which includes a defined a set of users who have been predefined and are, therefore, known to Fusion Applications. So, if you’re a user and you’re not part of that Fusion Apps ecosystem, you can’t access the pages. Period. That’s how Fusion Apps works to maintain its security and integrity. Secondly, you’re working pretty much solely with the Fusion Applications APIs data sources coming directly from Fusion Applications, which are also available to you when you’re creating bespoke visual apps. When you’re working with Fusion Applications in Visual Builder, you don’t have access to these business objects that give you access to your own customer database data through Visual Builder-generated REST APIs. Business objects are available only to bespoke visual applications in the hosted VB Cloud Service instance. So, your data sources are restricted to the Oracle Fusion Applications APIs and some third-party APIs that work within a narrow set of authentication mechanisms currently, although there are plans to expand this in the future. A mashup app that allows you now to access all these data sources while creating apps that leverage the Redwood Component System, so they look and work like Fusion Apps. They’re a highly popular option for our partners and customers. 16:25 Lois: So, to review, we have two different approaches. You can create a visual application using the for-fee, hosted Visual Builder Cloud Service/Stand-Alone or the one that comes with Oracle Integration Cloud, or you can use the extension architecture for Fusion applications, where you use the designer and create your extensions, and the code is delivered and deployed to Fusion applications code. You haven’t talked about JET yet though, Joe. What is that? Joe: So, JET is an abbreviation. It stands for Oracle JavaScript Extension Toolkit and JET is the underlying technology that makes Visual Builder, visual applications, and Visual Builder Extensions for Fusion Applications possible. Oracle JavaScript Extension Toolkit provides a module-based, open-source toolkit that leverages modern JavaScript, TypeScript, CSS3, and HTML5 to deliver web applications. It’s targeted at JavaScript developers working on... /episode/index/show/oracleuniversitypodcast/id/30394743

OCI AI Services 03/12/2024

OCI AI Services Listen to Lois Houston and Nikita Abraham, along with Senior Principal Product Manager Wes Prichard, as they explore the five core components of OCI AI services: language, speech, vision, document understanding, and anomaly detection, to help you make better sense of all that unstructured data around you. Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we spoke about OCI AI Portfolio, including AI and ML services, and the OCI AI infrastructure. Nikita: Yeah, and in today’s episode, we’re going to continue down a similar path and take a closer look at OCI AI services. 00:55 Lois: With us today is Senior Principal Product Manager, Wes Prichard. Hi Wes! It’s lovely to have you here with us. Hemant gave us a broad overview of the various OCI AI services last week, but we’re really hoping to get into each of them with you. So, let’s jump right in and start with the OCI Language service. What can you tell us about it? Wes: OCI Language analyzes unstructured text for you. It provides models trained on industry data to perform language analysis with no data science experience needed. 01:27 Nikita: What kind of big things can it do? Wes: It has five main capabilities. First, it detects the language of the text. It recognizes 75 languages, from Afrikaans to Welsh. It identifies entities, things like names, places, dates, emails, currency, organizations, phone numbers--14 types in all. It identifies the sentiment of the text, and not just one sentiment for the entire block of text, but the different sentiments for different aspects. 01:56 Nikita: What do you mean by that, Wes? Wes: So let's say you read a restaurant review that said, the food was great, but the service sucked. You'll get food with a positive sentiment and service with a negative sentiment. And it also analyzes the sentiment for every sentence. Lois: Ah, that’s smart. Ok, so we covered three capabilities. What else? Wes: It identifies key phrases in the text that represent the important ideas or subjects. And it classifies the general topic of the text from a list of 600 categories and subcategories. 02:27 Lois: Ok, and then there’s the OCI Speech service... Wes: OCI Speech is very straightforward. It locks the data in audio tracks by converting speech to text. Developers can use Oracle's time-tested acoustic language models to provide highly accurate transcription for audio or video files across multiple languages. OCI Speech automatically transcribes audio and video files into text using advanced deep learning techniques. There's no data science experience required. It processes data directly in object storage. And it generates timestamped, grammatically accurate transcriptions. 03:01 Nikita: What are some of the main features of OCI Speech? Wes: OCI Speech supports multiple languages, specifically English, Spanish, and Portuguese, with more coming in the future. It has batching support where multiple files can be submitted with a single call. It has blazing fast processing. It can transcribe hours of audio in less than 10 minutes. It does this by chunking up your audio into smaller segments, and transcribing each segment, and then joining them all back together into a single file. It provides a confidence score, both per word and per transcription. It punctuates transcriptions to make the text more readable and to allow downstream systems to process the text with less friction. And it has SRT file support. 03:45 Lois: SRT? What’s that? Wes: SRT is the most popular closed caption output file format. And with this SRT support, users can add closed captions to their video. OCI Speech makes transcribed text more readable to resemble how humans write. This is called normalization. And the service will normalize things like addresses, times, numbers, URLs, and more. It also does profanity filtering, where it can either remove, mask, or tag profanity and output text, where removing replaces the word with asterisks, and masking does the same thing, but it retains the first letter, and tagging will leave the word in place, but it provides tagging in the output data. 04:29 Nikita: And what about OCI Vision? What are its capabilities? Wes: Vision is a computed vision service that works on images, and it provides two main capabilities-- image analysis and document AI. Image analysis analyzes photographic images. Object detection is the feature that detects objects inside an image using a bounding box and assigning a label to each object with an accuracy percentage. Object detection also locates and extracts text that appears in the scene, like on a sign. Image classification will assign classification labels to the image by identifying the major features in the scene. One of the most powerful capabilities of image analysis is that, in addition to pretrained models, users can retrain the models with their own unique data to fit their specific needs. 05:20 Lois: So object detection and image classification are features of image analysis. I think I got it! So then what’s document AI? Wes: It's used for working with document images. You can use it to understand PDFs or document image types, like JPEG, PNG, and Tiff, or photographs containing textual information. 05:40 Lois: And what are its most important features? Wes: The features of document AI are text recognition, also known as OCR or optical character recognition. And this extracts text from images, including non-trivial scenarios, like handwritten texts, plus tilted, shaded, or rotated documents. Document classification classifies documents into 10 different types based on visual appearance, high-level features, and extracted keywords. This is useful when you need to process a document, based on its classification, like an invoice, a receipt, or a resume. Language detection analyzes the visual features of text to determine the language rather than relying on the text itself. Table extraction identifies tables in docs and extracts their content in tabular form. Key value extraction finds values for 13 common fields and line items in receipts, things like merchant name and transaction date. 06:41 Want to get the inside scoop on Oracle University? Head over to the Oracle University Learning Community. Attend exclusive events. Read up on the latest news. Get first-hand access to new products. Read the OU Learning Blog. Participate in Challenges. And stay up-to-date with upcoming certification opportunities. Visit mylearn.oracle.com to get started. 07:06 Nikita: Welcome back! Wes, I want to ask you about OCI Anomaly Detection. We discussed it a bit last week and it seems like such an intelligent and efficient service. Wes: Oracle Cloud Infrastructure Anomaly Detection identifies anomalies in time series data. Equipment sensors generate time series data, but all kinds of business metrics are also time-based. The unique feature of this service is that it finds anomalies, not just in a single signal, but across many signals at once. That's important because machines often generate multiple signals at once and the signals are often related. 07:42 Nikita: Ok you need to give us an example of this! Wes: Think of a pump that has an output pressure, a flow rate, an RPM, and an electrical current draw. When a pump's going to fail, anomalies may appear across several of those signals but at different times. OCI Anomaly Detection helps you to identify anomalies in a multivariate data set by taking advantage of the interrelationship among signals. The service contains algorithms for both multi-signal, as in multivariate, single signal, as in univariate anomaly detection, and it automatically determines which algorithm to use based on the training data provided. The multivariate algorithm is called MSET-2, which stands for Multivariate State Estimation technique, and it's unique to Oracle. 08:28 Lois: And the 2? Wes: The 2 in the name refers to the patented enhancements by Oracle labs that automatically identify and fix data quality issues resulting in fewer false alarms and more accurate results. Now unlike some of the other AI services, OCI Anomaly Detection is always trained on the customer's data. It's trained using actual historical data with no anomalies, and there can be as many different trained models as needed for different sets of signals. 08:57 Nikita: So where would one use a service like this? Wes: One of the most obvious applications of this service is for predictive maintenance. Early warning of a problem provides the opportunity to deploy maintenance resources and schedule downtime to minimize disruption to the business. 09:12 Lois: How would you train an OCI Anomaly Detection model? Wes: It's a simple four-step process to prepare a model that can be used for anomaly detection. The first step is to obtain training data from the system to be monitored. The data must contain no anomalies and should cover the normal range of values that would be experienced in a full business cycle. Second, the training data file is uploaded to an object storage bucket. Third, a data set is created for the training data. So a data set in this context is an object in the OCI Anomaly Detection service to manage data used for training and testing models. And fourth, the model is trained. A wizard in the user interface steps the user through the required inputs, such as the training data set and some training parameters like the target false alarm probability. 10:02 Lois: How would this service know about the data and whether the trained model is univariate or multivariate? Wes: When training OCI Anomaly Detection models, the user does not need to specify whether the intended model is for multivariate or univariate data. It does this detection automatically. For example, if a model is trained with 10 signals and 5 of those signals are determined to be correlated enough for multivariate anomaly detection, it will create an internal multivariate model for those signals. If the other five signals are not correlated with each other, it will create an internal univariate model for each one. From the user's perspective, the result will be a single OCI anomaly detection model for the 10 signals. But internally, the signals are treated differently based on the training. A user can also train a model on a single signal and it will result in a univariate model. 10:55 Lois: What does this OCI Anomaly Detection model training entail? How does it ensure that it does not have any false alarms? Wes: Training a model requires a single data file with no anomalies that should cover a complete business cycle, which means it should represent all the normal variations in the signal. During training, OCI Anomaly Detection will use a portion of the data for training and another portion for automated testing. The fraction used for each is specified when the model is trained. When model training is complete, it's best practice to do another test of the model with a data set containing anomalies to see if the anomalies are detected and if there are any false alarms. Based on the outcome, the user may want to retrain the model and specify a different false alarm probability, also called F-A-P or FAP. The FAP is the probability that the model would produce a false alarm. The false alarm probability can be thought of as the sensitivity of the model. The lower the false alarm probability, the less likelihood of it reporting a false alarm, but the less sensitive it will be to detecting anomalies. Selecting the right FAP is a business decision based on the need for sensitive detections balanced by the ability to tolerate false alarms. Once a model has been trained and the user is satisfied with its detection performance, it can then be used for inferencing. 12:23 Nikita: Inferencing? Is that what I think it is? Wes: New data is submitted to the model and OCI Anomaly Detection will respond with anomalies that are detected. The input data must contain the same signals that the model was trained on. So, for example, if the model was trained on signals A, B, C, and D, then for detection inferencing, the same four signals must be provided. No more, no less. 12:46 Lois: Where can I find the features of OCI Anomaly Detection that you mentioned? Wes: The training and inferencing features of OCI Anomaly Detection can be accessed through the OCI console. However, a human-driven interface is not efficient for most business scenarios. In most cases, automating the detection of anomalies through software is preferred to be able to process hundreds or thousands of signals using many trained models. The service provides multiple software interfaces for this purpose. Each trained model is accessible through a REST API and an HTTP endpoint. Additionally, programming language-specific SDKs are available for multiple languages, including Python. Using the Python SDK, data scientists can work with OCI Anomaly Detection for both training and inferencing in an OCI Data Science notebook. 13:37 Nikita: How can a data scientist take advantage of these capabilities? Wes: Well, you can write code against the REST API or use any of the various language SDKs. But for data scientists working in OCI Data Science, it makes sense to use Python. 13:51 Lois: That’s exciting! What does it take to use the Python SDK in a notebook… to be able to use the AI services? Wes: You can use a Notebook session in OCI Data Science to invoke the SDK for any of the AI services. This might be useful to generate new features for a custom model or simply as a way to consume the service using a familiar Python interface. But before you can invoke the SDK, you have to prepare the data science notebook session by supplying it with an API Signing Key. Signing Key is unique to a particular user and tenancy and authenticates that user to OCI when invoking the SDK. So therefore, you want to make sure you safeguard your Signing Key and never share it with another user. 14:34 Nikita: And where would I get my API Signing Key? Wes: You can obtain an API Signing Key from your user profile in the OCI Console. Then you save that key as a file to your local machine. The API Signing Key also provides commands to be added to a config file that the SDK expects to find in the environment, where the SDK code is executing. The config file then references the key file. Once these files are prepared on your local machine, you can upload them to the Notebook session, where you will execute SDK code for the AI service. The API Signing Key and config file can be reused with any of your notebook sessions, and the same files also work for all of the AI services. So, the files only need to be created once for each user and tenancy combination. 15:27 Lois: Thank you so much, Wes, for this really insightful discussion. To learn more about the topics covered today, you can visit mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. Nikita: And remember, that course prepares you for the Oracle Cloud Infrastructure AI Foundations Associate certification that you can take for free! So, don’t wait too long to check it out. Join us next week for another episode of the Oracle University Podcast. Until then, this is Nikita Abraham… Lois Houston: And Lois Houston, signing off! 16:03 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/30085203

The OCI AI Portfolio 03/05/2024

The OCI AI Portfolio Oracle has been actively focusing on bringing AI to the enterprise at every layer of its tech stack, be it SaaS apps, AI services, infrastructure, or data. In this episode, hosts Lois Houston and Nikita Abraham, along with senior instructors Hemant Gahankari and Himanshu Raj, discuss OCI AI and Machine Learning services. They also go over some key OCI Data Science concepts and responsible AI principles. Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hey everyone! In our last episode, we dove into Generative AI and Language Learning Models. Lois: Yeah, that was an interesting one. But today, we’re going to discuss the AI and machine learning services offered by Oracle Cloud Infrastructure, and we’ll look at the OCI AI infrastructure. Nikita: I’m also going to try and squeeze in a couple of questions on a topic I’m really keen about, which is responsible AI. To take us through all of this, we have two of our colleagues, Hemant Gahankari and Himanshu Raj. Hemant is a Senior Principal OCI Instructor and Himanshu is a Senior Instructor on AI/ML. So, let’s get started! 01:16 Lois: Hi Hemant! We’re so excited to have you here! We know that Oracle has really been focusing on bringing AI to the enterprise at every layer of our stack. Hemant: It all begins with data and infrastructure layers. OCI AI services consume data, and AI services, in turn, are consumed by applications. This approach involves extensive investment from infrastructure to SaaS applications. Generative AI and massive scale models are the more recent steps. Oracle AI is the portfolio of cloud services for helping organizations use the data they may have for the business-specific uses. Business applications consume AI and ML services. The foundation of AI services and ML services is data. AI services contain pre-built models for specific uses. Some of the AI services are pre-trained, and some can be additionally trained by the customer with their own data. AI services can be consumed by calling the API for the service, passing in the data to be processed, and the service returns a result. There is no infrastructure to be managed for using AI services. 02:37 Nikita: How do I access OCI AI services? Hemant: OCI AI services provide multiple methods for access. The most common method is the OCI Console. The OCI Console provides an easy to use, browser-based interface that enables access to notebook sessions and all the features of all the data science, as well as AI services. The REST API provides access to service functionality but requires programming expertise. And API reference is provided in the product documentation. OCI also provides programming language SDKs for Java, Python, TypeScript, JavaScript, .Net, Go, and Ruby. The command line interface provides both quick access and full functionality without the need for scripting. 03:31 Lois: Hemant, what are the types of OCI AI services that are available? Hemant: OCI AI services is a collection of services with pre-built machine learning models that make it easier for developers to build a variety of business applications. The models can also be custom trained for more accurate business results. The different services provided are digital assistant, language, vision, speech, document understanding, anomaly detection. 04:03 Lois: I know we’re going to talk about them in more detail in the next episode, but can you introduce us to OCI Language, Vision, and Speech? Hemant: OCI Language allows you to perform sophisticated text analysis at scale. Using the pre-trained and custom models, you can process unstructured text to extract insights without data science expertise. Pre-trained models include language detection, sentiment analysis, key phrase extraction, text classification, named entity recognition, and personal identifiable information detection. Custom models can be trained for named entity recognition and text classification with domain-specific data sets. In text translation, natural machine translation is used to translate text across numerous languages. Using OCI Vision, you can upload images to detect and classify objects in them. Pre-trained models and custom models are supported. In image analysis, pre-trained models perform object detection, image classification, and optical character recognition. In image analysis, custom models can perform custom object detection by detecting the location of custom objects in an image and providing a bounding box. The OCI Speech service is used to convert media files to readable texts that's stored in JSON and SRT format. Speech enables you to easily convert media files containing human speech into highly exact text transcriptions. 05:52 Nikita: That’s great. And what about document understanding and anomaly detection? Hemant: Using OCI document understanding, you can upload documents to detect and classify text and objects in them. You can process individual files or batches of documents. In OCR, document understanding can detect and recognize text in a document. In text extraction, document understanding provides the word level and line level text, and the bounding box, coordinates of where the text is found. In key value extraction, document understanding extracts a predefined list of key value pairs of information from receipts, invoices, passports, and driver IDs. In table extraction, document understanding extracts content in tabular format, maintaining the row and column relationship of cells. In document classification, the document understanding classifies documents into different types. The OCI Anomaly Detection service is a service that analyzes large volume of multivariate or univariate time series data. The Anomaly Detection service increases the reliability of businesses by monitoring their critical assets and detecting anomalies early with high precision. Anomaly Detection is the identification of rare items, events, or observations in data that differ significantly from the expectation. 07:34 Nikita: Where is Anomaly Detection most useful? Hemant: The Anomaly Detection service is designed to help with analyzing large amounts of data and identifying the anomalies at the earliest possible time with maximum accuracy. Different sectors, such as utility, oil and gas, transportation, manufacturing, telecommunications, banking, and insurance use Anomaly Detection service for their day-to-day activities. 08:02 Lois: Ok.. and the first OCI AI service you mentioned was digital assistant… Hemant: Oracle Digital Assistant is a platform that allows you to create and deploy digital assistants, which are AI driven interfaces that help users accomplish a variety of tasks with natural language conversations. When a user engages with the Digital Assistant, the Digital Assistant evaluates the user input and routes the conversation to and from the appropriate skills. Digital Assistant greets the user upon access. Upon user requests, list what it can do and provide entry points into the given skills. It routes explicit user requests to the appropriate skills. And it also handles interruptions to flows and disambiguation. It also handles requests to exit the bot. 09:00 Nikita: Excellent! Let’s bring Himanshu in to tell us about machine learning services. Hi Himanshu! Let’s talk about OCI Data Science. Can you tell us a bit about it? Himanshu: OCI Data Science is the cloud service focused on serving the data scientist throughout the full machine learning life cycle with support for Python and open source. The service has many features, such as model catalog, projects, JupyterLab notebook, model deployment, model training, management, model explanation, open source libraries, and AutoML. 09:35 Lois: Himanshu, what are the core principles of OCI Data Science? Himanshu: There are three core principles of OCI Data Science. The first one, accelerated. The first principle is about accelerating the work of the individual data scientist. OCI Data Science provides data scientists with open source libraries along with easy access to a range of compute power without having to manage any infrastructure. It also includes Oracle's own library to help streamline many aspects of their work. The second principle is collaborative. It goes beyond an individual data scientist’s productivity to enable data science teams to work together. This is done through the sharing of assets, reducing duplicative work, and putting reproducibility and auditability of models for collaboration and risk management. Third is enterprise grade. That means it's integrated with all the OCI Security and access protocols. The underlying infrastructure is fully managed. The customer does not have to think about provisioning compute and storage. And the service handles all the maintenance, patching, and upgrades so user can focus on solving business problems with data science. 10:50 Nikita: Let’s drill down into the specifics of OCI Data Science. So far, we know it’s cloud service to rapidly build, train, deploy, and manage machine learning models. But who can use it? Where is it? And how is it used? Himanshu: It serves data scientists and data science teams throughout the full machine learning life cycle. Users work in a familiar JupyterLab notebook interface, where they write Python code. And how it is used? So users preserve their models in the model catalog and deploy their models to a managed infrastructure. 11:25 Lois: Walk us through some of the key terminology that’s used. Himanshu: Some of the important product terminology of OCI Data Science are projects. The projects are containers that enable data science teams to organize their work. They represent collaborative work spaces for organizing and documenting data science assets, such as notebook sessions and models. Note that tenancy can have as many projects as needed without limits. Now, this notebook session is where the data scientists work. Notebook sessions provide a JupyterLab environment with pre-installed open source libraries and the ability to add others. Notebook sessions are interactive coding environment for building and training models. Notebook sessions run in a managed infrastructure and the user can select CPU or GPU, the compute shape, and amount of storage without having to do any manual provisioning. The other important feature is Conda environment. It's an open source environment and package management system and was created for Python programs. 12:33 Nikita: What is a Conda environment used for? Himanshu: It is used in the service to quickly install, run, and update packages and their dependencies. Conda easily creates, saves, loads, and switches between environments in your notebooks sessions. 12:46 Nikita: Earlier, you spoke about the support for Python in OCI Data Science. Is there a dedicated library? Himanshu: Oracle's Accelerated Data Science ADS SDK is a Python library that is included as part of OCI Data Science. ADS has many functions and objects that automate or simplify the steps in the data science workflow, including connecting to data, exploring, and visualizing data. Training a model with AutoML, evaluating models, and explaining models. In addition, ADS provides a simple interface to access the data science service mode model catalog and other OCI services, including object storage. 13:24 Lois: I also hear a lot about models. What are models? Himanshu: Models define a mathematical representation of your data and business process. You create models in notebooks, sessions, inside projects. 13:36 Lois: What are some other important terminologies related to models? Himanshu: The next terminology is model catalog. The model catalog is a place to store, track, share, and manage models. The model catalog is a centralized and managed repository of model artifacts. A stored model includes metadata about the provenance of the model, including Git-related information and the script. Our notebook used to push the model to the catalog. Models stored in the model catalog can be shared across members of a team, and they can be loaded back into a notebook session. The next one is model deployments. Model deployments allow you to deploy models stored in the model catalog as HTTP endpoints on managed infrastructure. 14:24 Lois: So, how do you operationalize these models? Himanshu: Deploying machine learning models as web applications, HTTP API endpoints, serving predictions in real time is the most common way to operationalize models. HTTP endpoints or the API endpoints are flexible and can serve requests for the model predictions. Data science jobs enable you to define and run a repeatable machine learning tasks on fully managed infrastructure. Nikita: Thanks for that, Himanshu. 14:57 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security, artificial intelligence, and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started. 15:25 Nikita: Welcome back! The Oracle AI Stack consists of AI services and machine learning services, and these services are built using AI infrastructure. So, let’s move on to that. Hemant, what are the components of OCI AI Infrastructure? Hemant: OCI AI Infrastructure is mainly composed of GPU-based instances. Instances can be virtual machines or bare metal machines. High performance cluster networking that allows instances to communicate to each other. Super clusters are a massive network of GPU instances with multiple petabytes per second of bandwidth. And a variety of fully managed storage options from a single byte to exabytes without upfront provisioning are also available. 16:14 Lois: Can we explore each of these components a little more? First, tell us, why do we need GPUs? Hemant: ML and AI needs lots of repetitive computations to be made on huge amounts of data. Parallel computing on GPUs is designed for many processes at the same time. A GPU is a piece of hardware that is incredibly good in performing computations. GPU has thousands of lightweight cores, all working on their share of data in parallel. This gives them the ability to crunch through extremely large data set at tremendous speed. 16:54 Nikita: And what are the GPU instances offered by OCI? Hemant: GPU instances are ideally suited for model training and inference. Bare metal and virtual machine compute instances powered by NVIDIA GPUs H100, A100, A10, and V100 are made available by OCI. 17:14 Nikita: So how do we choose what to train from these different GPU options? Hemant: For large scale AI training, data analytics, and high performance computing, bare metal instances BM 8 X NVIDIA H100 and BM 8 X NVIDIA A100 can be used. These provide up to nine times faster AI training and 30 times higher acceleration for AI inferencing. The other bare metal and virtual machines are used for small AI training, inference, streaming, gaming, and virtual desktop infrastructure. 17:53 Lois: And why would someone choose the OCI AI stack over its counterparts? Hemant: Oracle offers all the features and is the most cost effective option when compared to its counterparts. For example, BM GPU 4.8 version 2 instance costs just $4 per hour and is used by many customers. Superclusters are a massive network with multiple petabytes per second of bandwidth. It can scale up to 4,096 OCI bare metal instances with 32,768 GPUs. We also have a choice of bare metal A100 or H100 GPU instances, and we can select a variety of storage options, like object store, or block store, or even file system. For networking speeds, we can reach 1,600 GB per second with A100 GPUs and 3,200 GB per second with H100 GPUs. With OCI storage, we can select local SSD up to four NVMe drives, block storage up to 32 terabytes per volume, object storage up to 10 terabytes per object, file systems up to eight exabyte per file system. OCI File system employs five replicated storage located in different fault domains to provide redundancy for resilient data protection. HPC file systems, such as BeeGFS and many others are also offered. OCI HPC file systems are available on Oracle Cloud Marketplace and make it easy to deploy a variety of high performance file servers. 19:50 Lois: I think a discussion on AI would be incomplete if we don’t talk about responsible AI. We’re using AI more and more every day, but can we actually trust it? Hemant: For us to trust AI, it must be driven by ethics that guide us as well. Nikita: And do we have some principles that guide the use of AI? Hemant: AI should be lawful, complying with all applicable laws and regulations. AI should be ethical, that is it should ensure adherence to ethical principles and values that we uphold as humans. And AI should be robust, both from a technical and social perspective. Because even with the good intentions, AI systems can cause unintentional harm. AI systems do not operate in a lawless world. A number of legally binding rules at national and international level apply or are relevant to the development, deployment, and use of AI systems today. The law not only prohibits certain actions but also enables others, like protecting rights of minorities or protecting environment. Besides horizontally applicable rules, various domain-specific rules exist that apply to particular AI applications. For instance, the medical device regulation in the health care sector. In AI context, equality entails that the systems’ operations cannot generate unfairly biased outputs. And while we adopt AI, citizens right should also be protected. 21:30 Lois: Ok, but how do we derive AI ethics from these? Hemant: There are three main principles. AI should be used to help humans and allow for oversight. It should never cause physical or social harm. Decisions taken by AI should be transparent and fair, and also should be explainable. AI that follows the AI ethical principles is responsible AI. So if we map the AI ethical principles to responsible AI requirements, these will be like, AI systems should follow human-centric design principles and leave meaningful opportunity for human choice. This means securing human oversight. AI systems and environments in which they operate must be safe and secure, they must be technically robust, and should not be open to malicious use. The development, and deployment, and use of AI systems must be fair, ensuring equal and just distribution of both benefits and costs. AI should be free from unfair bias and discrimination. Decisions taken by AI to the extent possible should be explainable to those... /episode/index/show/oracleuniversitypodcast/id/30085113

Generative AI and Large Language Models 02/27/2024

Generative AI and Large Language Models In this week’s episode, Lois Houston and Nikita Abraham, along with Senior Instructor Himanshu Raj, take you through the extraordinary capabilities of Generative AI, a subset of deep learning that doesn’t make predictions but rather creates its own content. They also explore the workings of Large Language Models. Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! In our last episode, we went over the basics of deep learning. Today, we’ll look at generative AI and large language models, and discuss how they work. To help us with that, we have Himanshu Raj, Senior Instructor on AI/ML. So, let’s jump right in. Hi Himanshu, what is generative AI? 01:00 Himanshu: Generative AI refers to a type of AI that can create new content. It is a subset of deep learning, where the models are trained not to make predictions but rather to generate output on their own. Think of generative AI as an artist who looks at a lot of paintings and learns the patterns and styles present in them. Once it has learned these patterns, it can generate new paintings that resembles what it learned. 01:27 Lois: Let's take an example to understand this better. Suppose we want to train a generative AI model to draw a dog. How would we achieve this? Himanshu: You would start by giving it a lot of pictures of dogs to learn from. The AI does not know anything about what a dog looks like. But by looking at these pictures, it starts to figure out common patterns and features, like dogs often have pointy ears, narrow faces, whiskers, etc. You can then ask it to draw a new picture of a dog. The AI will use the patterns it learned to generate a picture that hopefully looks like a dog. But remember, the AI is not copying any of the pictures it has seen before but creating a new image based on the patterns it has learned. This is the basic idea behind generative AI. In practice, the process involves a lot of complex maths and computation, and there are different techniques and architectures that can be used, such as variational autoencoders (VAs) and Generative Adversarial Networks (GANs). 02:27 Nikita: Himanshu, where is generative AI used in the real world? Himanshu: Generative AI models have a wide variety of applications across numerous domains. For the image generation, generative models like GANs are used to generate realistic images. They can be used for tasks, like creating artwork, synthesizing images of human faces, or transforming sketches into photorealistic images. For text generation, large language models like GPT 3, which are generative in nature, can create human-like text. This has applications in content creation, like writing articles, generating ideas, and again, conversational AI, like chat bots, customer service agents. They are also used in programming for code generation and debugging, and much more. For music generation, generative AI models can also be used. They create new pieces of music after being trained on a specific style or collection of tunes. A famous example is OpenAI's MuseNet. 03:21 Lois: You mentioned large language models in the context of text-based generative AI. So, let’s talk a little more about it. Himanshu, what exactly are large language models? Himanshu: LLMs are a type of artificial intelligence models built to understand, generate, and process human language at a massive scale. They were primarily designed for sequence to sequence tasks such as machine translation, where an input sequence is transformed into an output sequence. LLMs can be used to translate text from one language to another. For example, an LLM could be used to translate English text into French. To do this job, LLM is trained on a massive data set of text and code which allows it to learn the patterns and relationships that exist between different languages. The LLM translates, “How are you?” from English to French, “Comment allez-vous?” It can also answer questions like, what is the capital of France? And it would answer the capital of France is Paris. And it will write an essay on a given topic. For example, write an essay on French Revolution, and it will come up with a response like with a title and introduction. 04:33 Lois: And how do LLMs actually work? Himanshu: So, LLM models are typically based on deep learning architectures such as transformers. They are also trained on vast amount of text data to learn language patterns and relationships, again, with a massive number of parameters usually in order of millions or even billions. LLMs have also the ability to comprehend and understand natural language text at a semantic level. They can grasp context, infer meaning, and identify relationships between words and phrases. 05:05 Nikita: What are the most important factors for a large language model? Himanshu: Model size and parameters are crucial aspects of large language models and other deep learning models. They significantly impact the model’s capabilities, performance, and resource requirement. So, what is model size? The model size refers to the amount of memory required to store the model's parameter and other data structures. Larger model sizes generally led to better performance as they can capture more complex patterns and representation from the data. The parameters are the numerical values of the model that change as it learns to minimize the model's error on the given task. In the context of LLMs, parameters refer to the weights and biases of the model's transformer layers. Parameters are usually measured in terms of millions or billions. For example, GPT-3, one of the largest LLMs to date, has 175 billion parameters making it extremely powerful in language understanding and generation. Tokens represent the individual units into which a piece of text is divided during the processing by the model. In natural language, tokens are usually words, subwords, or characters. Some models have a maximum token limit that they can process and longer text can may require truncation or splitting. Again, balancing model size, parameters, and token handling is crucial when working with LLMs. 06:29 Nikita: But what’s so great about LLMs? Himanshu: Large language models can understand and interpret human language more accurately and contextually. They can comprehend complex sentence structures, nuances, and word meanings, enabling them to provide more accurate and relevant responses to user queries. This model can generate human-like text that is coherent and contextually appropriate. This capability is valuable for context creation, automated writing, and generating personalized response in applications like chatbots and virtual assistants. They can perform a variety of tasks. Large language models are very versatile and adaptable to various industries. They can be customized to excel in applications such as language translation, sentiment analysis, code generation, and much more. LLMs can handle multiple languages making them valuable for cross-lingual tasks like translation, sentiment analysis, and understanding diverse global content. Large language models can be again, fine-tuned for a specific task using a minimal amount of domain data. The efficiency of LLMs usually grows with more data and parameters. 07:34 Lois: You mentioned the “sequence to sequence tasks” earlier. Can you explain the concept in simple terms for us? Himanshu: Understanding language is difficult for computers and AI systems. The reason being that words often have meanings based on context. Consider a sentence such as Jane threw the frisbee, and her dog fetched it. In this sentence, there are a few things that relate to each other. Jane is doing the throwing. The dog is doing the fetching. And it refers to the frisbee. Suppose we are looking at the word “it” in the sentence. As a human, we understand easily that “it” refers to the frisbee. But for a machine, it can be tricky. The goal in sequence problems is to find patterns, dependencies, or relationships within the data and make predictions, classification, or generate new sequences based on that understanding. 08:27 Lois: And where are sequence models mostly used? Himanshu: Some common example of sequence models includes natural language processing, which we call NLP, tasks such as machine translation, text generation sentiment analysis, language modeling involve dealing with sequences of words or characters. Speech recognition. Converting audio signals into text, involves working with sequences of phonemes or subword units to recognize spoken words. Music generation. Generating new music involves modeling musical sequences, nodes, and rhythms to create original compositions. Gesture recognition. Sequences of motion or hand gestures are used to interpret human movements for applications, such as sign language recognition or gesture-based interfaces. Time series analysis. In fields such as finance, economics, weather forecasting, and signal processing, time series data is used to predict future values, detect anomalies, and understand patterns in temporal data. 09:35 The Oracle University Learning Community is an excellent place to collaborate and learn with Oracle experts and fellow learners. Grow your skills, inspire innovation, and celebrate your successes. All your activities, from liking a post to answering questions and sharing with others, will help you earn a valuable reputation, badges, and ranks to be recognized in the community. Visit mylearn.oracle.com to get started. 10:03 Nikita: Welcome back! Himanshu, what would be the best way to solve those sequence problems you mentioned? Let’s use the same sentence, “Jane threw the frisbee, and her dog fetched it” as an example. Himanshu: The solution is transformers. It's like model has a bird's eye view of the entire sentence and can see how all the words relate to each other. This allows it to understand the sentence as a whole instead of just a series of individual words. Transformers with their self-attention mechanism can look at all the words in the sentence at the same time and understand how they relate to each other. For example, transformer can simultaneously understand the connections between Jane and dog even though they are far apart in the sentence. 10:52 Nikita: But how? Himanshu: The answer is attention, which adds context to the text. Attention would notice dog comes after frisbee, fetched comes after dog, and it comes after fetched. Transformer does not look at it in isolation. Instead, it also pays attention to all the other words in the sentence at the same time. But considering all these connections, the model can figure out that “it” likely refers to the frisbee. The most famous current models that are emerging in natural language processing tasks consist of dozens of transformers or some of their variants, for example, GPT or Bert. 11:32 Lois: I was looking at the AI Foundations course on MyLearn and came across the terms “prompt engineering” and “fine tuning.” Can you shed some light on them? Himanshu: A prompt is the input or initial text provided to the model to elicit a specific response or behavior. So, this is something which you write or ask to a language model. Now, what is prompt engineering? So prompt engineering is the process of designing and formulating specific instructions or queries to interact with a large language model effectively. In the context of large language models, such as GPT 3 or Burt, prompts are the input text or questions given to the model to generate responses or perform specific tasks. The goal of prompt engineering is to ensure that the language model understands the user's intent correctly and provide accurate and relevant responses. 12:26 Nikita: That sounds easy enough, but fine tuning seems a bit more complex. Can you explain it with an example? Himanshu: Imagine you have a versatile recipe robot named chef bot. Suppose that chef bot is designed to create delicious recipes for any dish you desire. Chef bot recognizes the prompt as a request for a pizza recipe, and it knows exactly what to do. However, if you want chef bot to be an expert in a particular type of cuisine, such as Italian dishes, you fine-tune chef bot for Italian cuisine by immersing it in a culinary crash course filled with Italian cookbooks, traditional Italian recipes, and even Italian cooking shows. During this process, chef bot becomes more specialized in creating authentic Italian recipes, and this option is called fine tuning. LLMs are general purpose models that are pre-trained on large data sets but are often fine-tuned to address specific use cases. When you combine prompt engineering and fine tuning, and you get a culinary wizard in chef bot, a recipe robot that is not only great at understanding specific dish requests but also capable of following a specific dish requests and even mastering the art of cooking in a particular culinary style. 13:47 Lois: Great! Now that we’ve spoken about all the major components, can you walk us through the life cycle of a large language model? Himanshu: The life cycle of a Large Language Model, LLM, involves several stages, from its initial pre-training to its deployment and ongoing refinement. The first of this lifecycle is pre-training. The LLM is initially pre-trained on a large corpus of text data from the internet. During pre-training, the model learns grammar, facts, reasoning abilities, and general language understanding. The model predicts the next word in a sentence given the previous words, which helps it capture relationships between words and the structure of language. The second phase is fine tuning initialization. After pre-training, the model's weights are initialized, and it's ready for task-specific fine tuning. Fine tuning can involve supervised learning on labeled data for specific tasks, such as sentiment analysis, translation, or text generation. The model is fine-tuned on specific tasks using a smaller domain-specific data set. The weights from pre-training are updated based on the new data, making the model task aware and specialized. The next phase of the LLM life cycle is prompt engineering. So this phase craft effective prompts to guide the model's behavior in generating specific responses. Different prompt formulations, instructions, or context can be used to shape the output. 15:13 Nikita: Ok… we’re with you so far. What’s next? Himanshu: The next phase is evaluation and iteration. So models are evaluated using various metrics to access their performance on specific tasks. Iterative refinement involves adjusting model parameters, prompts, and fine tuning strategies to improve results. So as a part of this step, you also do few shot and one shot inference. If needed, you further fine tune the model with a small number of examples. Basically, few shot or a single example, one shot for new tasks or scenarios. Also, you do the bias mitigation and consider the ethical concerns. These biases and ethical concerns may arise in models output. You need to implement measures to ensure fairness in inclusivity and responsible use. 16:07 Himanshu: The next phase in LLM life cycle is deployment. Once the model has been fine-tuned and evaluated, it is deployed for real world applications. Deployed models can perform tasks, such as text generation, translation, summarization, and much more. You also perform monitoring and maintenance in this phase. So you continuously monitor the model's performance and output to ensure it aligns with desired outcomes. You also periodically update and retrain the model to incorporate new data and to adapt to evolving language patterns. This overall life cycle can also consist of a feedback loop, whether you gather feedbacks from users and incorporate it into the model’s improvement process. You use this feedback to further refine prompts, fine tuning, and overall model behavior. RLHF, which is Reinforcement Learning with Human Feedback, is a very good example of this feedback loop. You also research and innovate as a part of this life cycle, where you continue to research and develop new techniques to enhance the model capability and address different challenges associated with it. 17:19 Nikita: As we’re talking about the LLM life cycle, I see that fine tuning is not only about making an LLM task specific. So, what are some other reasons you would fine tune an LLM model? Himanshu: The first one is task-specific adaptation. Pre-trained language models are trained on extensive and diverse data sets and have good general language understanding. They excel in language generation and comprehension tasks, though the broad understanding of language may not lead to optimal performance in specific task. These models are not task specific. So the solution is fine tuning. The fine tuning process customizes the pre-trained models for a specific task by further training on task-specific data to adapt the model's knowledge. The second reason is domain-specific vocabulary. Pre-trained models might lack knowledge of specific words and phrases essential for certain tasks in fields, such as legal, medical, finance, and technical domains. This can limit their performance when applied to domain-specific data. Fine tuning enables the model to adapt and learn domain-specific words and phrases. These words could be, again, from different domains. 18:35 Himanshu: The third reason to fine tune is efficiency and resource utilization. So fine tuning is computationally efficient compared to training from scratch. Fine tuning reuses the knowledge from pre-trained models, saving time and resources. Fine tuning requires fewer iterations to achieve task-specific competence. Shorter training cycles expedite the model development process. It conserves computational resources, such as GPU memory and processing power. Fine tuning is efficient in quicker model deployment. It has faster time to production for real world applications. Fine tuning is, again, a scalable enabling adaptation to various tasks with the same base model, which further reduce resource demands, and it leads to cost saving for research and development. The fourth reason to fine tune is of ethical concerns. Pre-trained models learns from diverse data. And those potentially inherit different biases. Fine tune might not completely eliminate biases. But careful curation of task specific data ensures avoiding biased or harmful vocabulary. The responsible uses of domain-specific terms promotes ethical AI applications. 19:53 Lois: Thank you so much, Himanshu, for spending time with us. We had such a great time learning from you. If you want to learn more about the topics discussed today, head over to mylearn.oracle.com and get started on our free AI Foundations course. Nikita: Yeah, we even have a detailed... /episode/index/show/oracleuniversitypodcast/id/30084878

Deep Learning 02/20/2024

Deep Learning Did you know that the concept of deep learning goes way back to the 1950s? However, it is only in recent years that this technology has created a tremendous amount of buzz (and for good reason!). A subset of machine learning, deep learning is inspired by the structure of the human brain, making it fascinating to learn about. In this episode, Lois Houston and Nikita Abraham interview Senior Principal OCI Instructor Hemant Gahankari about deep learning concepts, including how Convolution Neural Networks work, and help you get your deep learning basics right. Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! Last week, we covered the new MySQL HeatWave Implementation Associate certification. So do go check out that episode if it interests you. Lois: That was a really interesting discussion for sure. Today, we’re going to focus on the basics of deep learning with our Senior Principal OCI Instructor, Hemant Gahankari. 00:58 Nikita: Hi Hemant! Thanks for being with us today. So, to get started, what is deep learning? Hemant: Deep learning is a subset of machine learning that focuses on training Artificial Neural Networks to solve a task at hand. Say, for example, image classification. A very important quality of the ANN is that it can process raw data like pixels of an image and extract patterns from it. These patterns are treated as features to predict the outcomes. Let us say we have a set of handwritten images of digits 0 to 9. As we know, everyone writes the digits in a slightly different way. So how do we train a machine to identify a handwritten digit? For this, we use ANN. ANN accepts image pixels as inputs, extracts patterns like edges and curves and so on, and correlates these patterns to predict an outcome. That is what digit does the image has in this case. 02:04 Lois: Ok, so what you’re saying is given a bunch of pixels, ANN is able to process pixel data, learn an internal representation of the data, and predict outcomes. That’s so cool! So, why do we need deep learning? Hemant: We need to specify features while we train machine learning algorithm. With deep learning, features are automatically extracted from the data. Internal representation of features and their combinations is built to predict outcomes by deep learning algorithms. This may not be feasible manually. Deep learning algorithms can make use of parallel computations. For this, usually data is split into small batches and process parallelly. So these algorithms can process large amount of data in a short time to learn the features and their combinations. This leads to scalability and performance. In short, deep learning complements machine learning algorithms for complex data for which features cannot be described easily. 03:13 Nikita: What can you tell us about the origins of deep learning? Hemant: Some of the deep learning concepts like artificial neuron, perceptron, and multilayer perceptron existed as early as 1950s. One of the most important concept of using backpropagation for training ANN came in 1980s. In 1990s, convolutional neural network were also introduced for image analysis task. Starting 2000, GPUs were introduced. And 2010 onwards, GPUs became cheaper and widely available. This fueled the widespread adoption of deep learning uses like computer vision, natural language processing, speech recognition, text translation, and so on. In 2012, major networks like AlexNet and Deep-Q Network were built. 2016 onward, generative use cases of the deep learning also started to come up. Today, we have widely adopted deep learning for a variety of use cases, including large language models and many other types of generative models. 04:29 Lois: Hemant, what are various applications of deep learning algorithms? Hemant: Deep learning algorithms are targeted at a variety of data and applications. For data, we have images, videos, text, and audio. For images, applications can be image classification, object detection, and so on. For textual data, applications are to translate the text or detect a sentiment of a text. For audio, the applications can be music generation, speech to text, and so on. 05:08 Lois: It's important that we select the right deep learning algorithm based on the data and application, right? So how do we do that? Hemant: For image task like image classification, object detection, image segmentation, or facial recognition, CNN is a suitable architecture. For text, we have a choice of the latest transformers or LSTM or even RNN. For generative tasks like text summarization, question answering, transformers is a good choice. For generating images, text to image generation, transformers, GANs, or diffusion models are available choice. 05:51 Nikita: Let’s dive a little deeper into Artificial Neural Networks. Can you tell us more about them, Hemant? Hemant: Artificial Neural Networks are inspired by the human brain. They are made up of interconnected nodes called as neurons. Nikita: And how are inputs processed by a neuron? Hemant: In ANN, we assign weights to the connection between neurons. Weighted inputs are added up. And if the sum crosses a specified threshold, the neuron is fired. And the outputs of a layer of neuron become an input to another layer. 06:27 Lois: Hemant, tell us about the building blocks of ANN so we understand this better. Hemant: So first, building block is layers. We have input layer, output layer, and multiple hidden layers. The input layer and output layer are mandatory. And the hidden layers are optional. The second unit is neurons. Neurons are computational units, which accept an input and produce an output. Weights determine the strength of connection between neurons. So the connection could be between input and a neuron, or it could be between a neuron and another neuron. Activation functions work on the weighted sum of inputs to a neuron and produce an output. Additional input to the neuron that allows a certain degree of flexibility is called as a bias. 07:27 Nikita: I think we’ve got the components of ANN straight but maybe you should give us an example. You mentioned this example earlier…of needing to train ANN to recognize handwritten digits from images. How would we go about that? Hemant: For that, we have to collect a large number of digit images, and we need to train ANN using these images. So, in this case, the images consist of 28 by 28 pixels which act as input layer. For the output, we have neurons-- 10 neurons which represent digits 0 to 9. And we have multiple hidden layers. So, in this case, we have two hidden layers which are consisting of 16 neurons each. The hidden layers are responsible for capturing the internal representation of the raw image data. And the output layer is responsible for producing the desired outcomes. So, in this case, the desired outcome is the prediction of whether the digit is 0 or 1 or up to digit 9. So how do we train this particular ANN? So the first thing we use the backpropagation algorithm. During training, we show an image to the ANN. Let us say it is an image of digit 2. So we expect output neuron for digit 2 to fire. But in real, let us say output neuron of a digit 6 fired. 09:12 Lois: So, then, what do we do? Hemant: We know that there is an error. So to correct an error, we adjust the weights of the connection between neurons based on a calculation, which we call as backpropagation algorithm. By showing thousands of images and adjusting the weights iteratively, ANN is able to predict correct outcome for most of the input images. This process of adjusting weights through backpropagation is called as model training. 09:48 Do you have an idea for a new course or learning opportunity? We’d love to hear it! Visit the Oracle University Learning Community and share your thoughts with us on the Idea Incubator. Your suggestion could find a place in future development projects! Visit mylearn.oracle.com to get started. 10:09 Nikita: Welcome back! Let’s move on to CNN. Hemant, what is a Convolutional Neural Network? Hemant: CNN is a type of deep learning model specifically designed for processing and analyzing grid-like data, such as images and videos. In the ANN, the input image is converted to a single dimensional array and given as an input to the network. But that does not work well with the image data because image data is inherently two dimensional. CNN works better with two dimensional data. The role of the CNN is to reduce the image into a form, which is easier to process and without losing features, which are critical for getting a good prediction. 10:53 Lois: A CNN has different layers, right? Could you tell us a bit about them? Hemant: The first one is input layer. Input layer is followed by feature extraction layers, which is a combination and repetition of multiple feature extraction layers, including convolutional layer with ReLu activation and a pooling layer. And this is followed by a classification layer. These are the fully connected output layers, where the classification occurs as output classes. The feature extraction layers play a vital role in image classification. 11:33 Nikita: Can you explain these layers with an example? Hemant: Let us say we have a robot to inspect a house and tell us what type of a house it is. It uses many tools for this purpose. The first tool is a blueprint detector. It scans different parts of the house, like walls, floors, or windows, and looks for specific patterns or features. The second tool is a pattern highlighter. This tool marks areas detected by the blueprint detector. The next tool is a summarizer. It tries to capture the most significant features of every room. The next tool is house expert, which looks at all the highlighted patterns and features, and tries to understand the house. The next tool is a guess maker. It assigns probabilities to the different possible house types. And finally, the quality checker randomly checks different parts of the analysis to make sure that the robot doesn't rely too much on any single piece of information. 12:40 Nikita: Ok, so how are you mapping these to the feature extraction layers? Hemant: Similar to blueprint detector, we have a convolutional layer. This layer applies convolutional operations to the input image using small filters known as kernels. Each filter slides across the input image to detect specific features, such as edges, corners, or textures. Similar to pattern highlighter, we have a activation function. The activation function allows the network to learn more complex and non-linear relationships in the data. Pooling layer is similar to room summarizer. Pooling helps reduce the spatial dimensions of the feature maps generated by the convolutional layers. Similar to house expert, we have a fully connected layer, which is responsible for making final predictions or classifications based on the learned features. Softmax layer converts the output of the last fully connected layers into probability scores. The class with the highest probability is the predicted class. This is similar to the guess maker. And finally, we have the dropout layer. This layer is a regularization technique used to prevent overfitting in the network. This has the same role as that of a quality checker. 14:05 Lois: Do CNNs have any limitations that we need to be aware of? Hemant: Training CNNs on large data sets can be computationally expensive and time consuming. CNNs are susceptible to overfitting, especially when the training data is limited or imbalanced. CNNs are considered black box models making it difficult to interpret. And CNNs can be sensitive to small changes in the input leading to unstable predictions. 14:33 Nikita: And what are the top applications of CNN? Hemant: One of the most widely used applications of CNNs is image classification. For example, classifying whether an image contains a specific object, say cat or a dog. CNNs are used for object detection tasks. The goal here is to draw bounding boxes around objects in an image. CNNs can perform pixel level segmentation, where each pixel in the image is labeled to represent different objects or regions. CNNs are employed for face recognition tasks as well, identifying and verifying individuals based on facial features. CNNs are widely used in medical image analysis, helping with tasks like tumor detection, diagnosis, and classification of various medical conditions. CNNs play an important role in the development of self-driving cars, helping them to recognize and understand the road traffic signs, pedestrians, and other vehicles. And CNNs are applied in analyzing satellite images and remote sensing data for tasks, such as land cover classification and environmental monitoring. 15:50 Nikita: Hemant, let’s talk about sequence models. What are they and what are they used for? Hemant: Sequence models are used to solve problems, where the input data is in the form of sequences. The sequences are ordered lists of data points or events. The goal in sequence models is to find patterns and dependencies within the data and make predictions, classifications, or even generate new sequences. 16:17 Lois: Can you give us some examples of sequence models? Hemant: Some common examples of the sequence models are in natural language processing, deep learning models are used for tasks, such as machine translation, sentiment analysis, or text generation. In speech recognition, deep learning models are used to convert a recorded audio into text. In deep learning models, can generate new music or create original compositions. Even sequences of hand gestures are interpreted by deep learning models for applications like sign language recognition. In fields like finance or weather prediction, time series data is used to predict future values. 17:03 Nikita: Which deep learning models can be used to work with sequence data? Hemant: Recurrent Neural Networks, abbreviated as RNNs, are a class of neural network architectures specifically designed to handle sequential data. Unlike traditional feedforward neural network, RNNs have a feedback loop that allows information to persist across different timesteps. The key features of RNN is their ability to maintain an internal state often referred to as a hidden state or memory, which is updated as the network processes each element in the input sequence. The hidden state is then used as input to the network for the next time step, allowing the model to capture dependencies and patterns in the data that are spread across time. 17:58 Nikita: Are there various types of RNNs? Hemant: There are different types of RNN architecture based on application. One of them is one to one. This is like feed forward neural network and is not suited for sequential data. A one to many model produces multiple output values for one input value. Music generation or sequence generation are some applications using this architecture. A many to one model produces one output value after receiving multiple input values. Example is sentiment analysis based on the review. Many to many model produces multiple output values for multiple input values. Examples are machine translation and named entity recognition. RNN does not perform that well when it comes to capturing long term dependencies. This is due to the vanishing gradients problem, which is overcome by using LSTM model. 19:07 Lois: Another acronym. What is LSTM, Hemant? Hemant: Long Short-Term memory, abbreviated as LSTM, works by using a specialized memory cell and a gating mechanisms to capture long term dependencies in the sequential data. The key idea behind LSTM is to selectively remember or forget information over time, enabling the model to maintain relevant information over long sequences, which helps overcome the vanishing gradients problem. 19:40 Nikita: Can you take us, step-by-step, through the working of LSTM? Hemant: At each timestep, the LSTM takes an input vector representing the current data point in the sequence. The LSTM also receives the previous hidden state and cell state. These represent what the LSTM has remembered and forgotten up to the current point in the sequence. The core of the LSTM lies in its gating mechanisms, which include three gates: the input gate, the forget gate, and the output gate. These gates are like the filters that control the flow of information within the LSTM cell. The input gate decides what new information from the current input should be added to the memory cell. The forget gate determines what information in the current memory cell should be discarded or forgotten. The output gate regulates how much of the current memory cell should be exposed as the output of the current time step. Using the information from the input gate and forget gate, the LSTM updates its cell state. The LSTM then uses the output gate to produce the current hidden state, which becomes the output of the LSTM for the next time step. 21:12 Lois: Thank you, Hemant, for joining us in this episode of the Oracle University Podcast. I learned so much today. If you want to learn more about deep learning, visit mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. And remember, the AI Foundations course and certification are free. So why not get started now? Nikita: Right, Lois. In our next episode, we will discuss generative AI and language learning models. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 21:45 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/29991598

Everything You Need to Know About the MySQL HeatWave Implementation Associate Certification 02/13/2024

Everything You Need to Know About the MySQL HeatWave Implementation Associate Certification What is MySQL HeatWave? How do I get certified in it? Where do I start? Listen to Lois Houston and Nikita Abraham, along with MySQL Developer Scott Stroz, answer all these questions and more on this week's episode of the Oracle University Podcast. MySQL Document Store: https://oracleuniversitypodcast.libsyn.com/mysql-document-store Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! For the last two weeks, we’ve been having really exciting discussions on everything AI. We covered the basics of artificial intelligence and machine learning, and we’re taking a short break from that today to talk about the new MySQL HeatWave Implementation Associate Certification with MySQL Developer Advocate Scott Stroz. 00:59 Nikita: You may remember Scott from an episode last year where he came on to discuss MySQL Document Store. We’ll post the link to that episode in the show notes so you can listen to it if you haven’t already. Lois: Hi Scott! Thanks for joining us again. Before diving into the certification, tell us, what is MySQL HeatWave? 01:19 Scott: Hi Lois, Hi Niki. I’m so glad to be back. So, MySQL HeatWave Database Service is a fully managed database that is capable of running transactional and analytic queries in a single database instance. This can be done across data warehouses and data lakes. We get all the benefits of analytic queries without the latency and potential security issues of performing standard extract, transform, and load, or ETL, operations. Some other MySQL HeatWave database service features are automated system updates and database backups, high availability, in-database machine learning with AutoML, MySQL Autopilot for managing instance provisioning, and enhanced data security. HeatWave is the only cloud database service running MySQL that is built, managed, and supported by the MySQL Engineering team. 02:14 Lois: And where can I find MySQL HeatWave? Scott: MySQL HeatWave is only available in the cloud. MySQL HeatWave instances can be provisioned in Oracle Cloud Infrastructure or OCI, Amazon Web Services (AWS), and Microsoft Azure. Now, some features though are only available in Oracle Cloud, such as access to MySQL Document Store. 02:36 Nikita: Scott, you said MySQL HeatWave runs transactional and analytic queries in a single instance. Can you elaborate on that? Scott: Sure, Niki. So, MySQL HeatWave allows developers, database administrators, and data analysts to run transactional queries (OLTP) and analytic queries (OLAP). OLTP, or online transaction processing, allows for real-time execution of database transactions. A transaction is any kind of insertion, deletion, update, or query of data. Most DBAs and developers work with this kind of processing in their day-to-day activities. OLAP, or online analytical processing, is one way to handle multi-dimensional analytical queries typically used for reporting or data analytics. OLTP system data must typically be exported, aggregated, and imported into an OLAP system. This procedure is called ETL as I mentioned – extract, transform, and load. With large datasets, ETL processes can take a long time to complete, so analytic data could be “old” by the time it is available in an OLAP system. There is also an increased security risk in moving the data to an external source. 03:56 Scott: MySQL HeatWave eliminates the need for time-consuming ETL processes. We can actually get real-time analytics from our data since HeatWave allows for OLTP and OLAP in a single instance. I should note, this also includes analytic from JSON data that may be stored in the database. Another advantage is that applications can use MySQL HeatWave without changing any of the application code. Developers only need to point their applications at the MySQL HeatWave databases. MySQL HeatWave is fully compatible with on-premise MySQL instances, which can allow for a seamless transition to the cloud. And one other thing. When MySQL HeatWave has OLAP features enabled, MySQL can determine what type of query is being executed and route it to either the normal database system or the in-memory database. 04:52 Lois: That’s so cool! And what about the other features you mentioned, Scott? Automated updates and backups, high availability… Scott: Right, Lois. But before that, I want to tell you about the in-memory query accelerator. MySQL HeatWave offers a massively parallel, in-memory hybrid columnar query processing engine. It provides high performance by utilizing algorithms for distributed query processing. And this query processing in MySQL HeatWave is optimized for cloud environments. MySQL HeatWave can be configured to automatically apply system updates, so you will always have the latest and greatest version of MySQL. Then, we have automated backups. By this, I mean MySQL HeatWave can be configured to provide automated backups with point-in-time recovery to ensure data can be restored to a particular date and time. MySQL HeatWave also allows us to define a retention plan for our database backups, that means how long we keep the backups before they are deleted. High availability with MySQL HeatWave allows for more consistent uptime. When using high availability, MySQL HeatWave instances can be provisioned across multiple availability domains, providing automatic failover for when the primary node becomes unavailable. All availability domains within a region are physically separated from each other to mitigate the possibility of a single point of failure. 06:14 Scott: We also have MySQL Lakehouse. Lakehouse allows for the querying of data stored in object storage in various formats. This can be CSV, Parquet, Avro, or an export format from other database systems. And basically, we point Lakehouse at data stored in Oracle Cloud, and once it’s ingested, the data can be queried just like any other data in a database. Lakehouse supports querying data up to half a petabyte in size using the HeatWave engine. And this allows users to take advantage of HeatWave for non-MySQL workloads. MySQL AutoPilot is a part of MySQL HeatWave and can be used to predict the number of HeatWave nodes a system will need and automatically provision them as part of a cluster. AutoPilot has features that can handle automatic thread pooling and database shape predicting. A “shape” is one of the many different CPU, memory, and ethernet traffic configurations available for MySQL HeatWave. MySQL HeatWave includes some advanced security features such as asymmetric encryption and automated data masking at query execution. As you can see, there are a lot of features covered under the HeatWave umbrella! 07:31 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started. 08:02 Nikita: Welcome back! Now coming to the certification, who can actually take this exam, Scott? Scott: The MySQL HeatWave Implementation Associate Certification Exam is designed specifically for administrators and data scientists who want to provision, configure, and manage MySQL HeatWave for transactions, analytics, machine learning, and Lakehouse. 08:22 Nikita: Can someone who’s just graduated, say an engineering graduate interested in data analytics, take this certification? Are there any prerequisites? What are the career prospects for them? Scott: There are no mandatory prerequisites, but anyone who wants to take the exam should have experience with MySQL HeatWave and other aspects of OCI, such as virtual cloud networks and identity and security processes. Also, the learning path on MyLearn will be extremely helpful when preparing for the exam, but you are not required to complete the learning path before registering for the exam. The exam focuses more on getting MySQL HeatWave running (and keeping it running) than accessing the data. That doesn’t mean it is not helpful for someone interested in data analytics. I think it can be helpful for data analysts to understand how the system providing the data functions, even if it is at just a high level. It is also possible that data analysts might be responsible for setting up their own systems and importing and managing their own data. 09:23 Lois: And how do I get started if I want to get certified on MySQL HeatWave? Scott: So, you’ll first need to go to mylearn.oracle.com and look for the “Become a MySQL HeatWave Implementation Associate” learning path. The learning path consists of over 10 hours of training across 8 different courses. These courses include “Getting Started with MySQL HeatWave Database Service,” which offers an introduction to some Oracle Cloud functionality such as security and networking, as well as showing one way to connect to a MySQL HeatWave instance. Another course demonstrates how to configure MySQL instances and copy that configuration to other instances. Other courses cover how to migrate data into MySQL HeatWave, set up and manage high availability, and configure HeatWave for OLAP. You’ll find labs where you can perform hands-on activities, student and activity guides, and skill checks to test yourself along the way. And there’s also the option to Ask the Instructor if you have any questions you need answers to. You can also access the Oracle University Learning Community and discuss topics with others on the same journey. The learning path includes a practice exam to check your readiness to pass the certification exam. 10:33 Lois: Yeah, and remember, access to the entire learning path is free so there’s nothing stopping you from getting started right away. Now Scott, what does the certification test you on? Scott: The MySQL HeatWave Implementation exam, which is an associate-level exam, covers various topics. It will validate your ability to identify key features and benefits of MySQL HeatWave and describe the MySQL HeatWave architecture; identify Virtual Cloud Network (VCN) requirements and the different methods of connecting to a MySQL HeatWave instance; manage the automatic backup process and restore database systems from these backups; configure and manage read replicas and inbound replication channels; import data into MySQL HeatWave; configure and manage high availability and clustering of MySQL HeatWave instances. I know this seems like a lot of different topics. That is why we recommend anyone interested in the exam follow the learning path. It will help make sure you have the exposure to all the topics that are covered by the exam. 11:35 Lois: Tell us more about the certification process itself. Scott: While the courses we already talked about are valuable when preparing for the exam, nothing is better than hands-on experience. We recommend that candidates have hands-on experience with MySQL HeatWave with real-world implementations. The format of the exam is Multiple Choice. It is 90 minutes long and consists of 65 questions. When you’ve taken the recommended training and feel ready to take the certification exam, you need to purchase the exam and register for it. You go through the section on things to do before the exam and the exam policies, and then all that’s left to do is schedule the date and time of the exam according to when is convenient for you. 12:16 Nikita: And once you’ve finished the exam? Scott: When you’re done your score will be displayed on the screen when you finish the exam. You will also receive an email indicating whether you passed or failed. You can view your exam results and full score report in Oracle CertView, Oracle’s certification portal. From CertView, you can download and print your eCertificate and even share your newly earned badge on places like Facebook, Twitter, and LinkedIn. 12:38 Lois: And for how long does the certification remain valid, Scott? Scott: There is no expiration date for the exam, so the certification will remain valid for as long as the material that is covered remains relevant. 12:49 Nikita: What’s the next step for me after I get this certification? What other training can I take? Scott: So, because this exam is an associate level exam, it is kind of a stepping stone along a person’s MySQL training. I do not know if there are plans for a professional level exam for HeatWave, but Oracle University has several other training programs that are MySQL-specific. There are learning paths to help prepare for the MySQL Database Administrator and MySQL Database Developer exams. As with the HeatWave learning paths, the learning paths for these exams include video tutorials, hands-on activities, skill checks, and practice exams. 13:27 Lois: I think you’ve told us everything we need to know about this certification, Scott. Are there any parting words you might have? Scott: We know that the whole process of training and getting certified may seem daunting, but we’ve really tried to simplify things for you with the “Become a MySQL HeatWave Implementation Associate” learning path. It not only prepares you for the exam but also gives you experience with features of MySQL HeatWave that will surely be valuable in your career. 13:51 Lois: Thanks so much, Scott, for joining us today. Nikita: Yeah, we’ve had a great time with you. Scott: Thanks for having me. Lois: Next week, we’ll get back to our focus on AI with a discussion on deep learning. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off. 14:07 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/29843318

Machine Learning 02/06/2024

Machine Learning Does machine learning feel like too convoluted a topic? Not anymore! Listen to hosts Lois Houston and Nikita Abraham, along with Senior Principal OCI Instructor Hemant Gahankari, talk about foundational machine learning concepts and dive into how supervised learning, unsupervised learning, and reinforcement learning work. Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode. --------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence and we’re going to take it a step further today by talking about some foundational machine learning concepts. After that, we’ll discuss the three main types of machine learning models: supervised learning, unsupervised learning, and reinforcement learning. 00:57 Lois: Hemant Gahankari, a Senior Principal OCI Instructor, joins us for this episode. Hi Hemant! Let’s dive right in. What is machine learning? How does it work? Hemant: Machine learning is a subset of artificial intelligence that focuses on creating computer systems that can learn and predict outcomes from given examples without being explicitly programmed. It is powered by algorithms that incorporate intelligence into machines by automatically learning from a set of examples usually provided as data. 01:34 Nikita: Give us a few examples of machine learning… so we can see what it can do for us. Hemant: Machine learning is used by all of us in our day-to-day life. When we shop online, we get product recommendations based on our preferences and our shopping history. This is powered by machine learning. We are notified about movies recommendations based on our viewing history and choices of other similar viewers. This too is driven by machine learning. While browsing emails, we are warned of a spam mail because machine learning classifies whether the mail is spam or not based on its content. In the increasingly popular self-driving cars, machine learning is responsible for taking the car to its destination. 02:24 Lois: So, how does machine learning actually work? Hemant: Let us say we have a computer and we need to teach the computer to differentiate between a cat and a dog. We do this by describing features of a cat or a dog. Dogs and cats have distinguishing features. For example, the body color, texture, eye color are some of the defining features which can be used to differentiate a cat from a dog. These are collectively called as input data. We also provide a corresponding output, which is called as a label, which can be a dog or a cat in this case. By describing a specific set of features, we can say that it is a cat or a dog. Machine learning model is first trained with the data set. Training data set consists of a set of features and output labels, and is given as an input to the machine learning model. During the process of training, machine learning model learns the relation between input features and corresponding output labels from the provided data. Once the model learns from the data, we have a trained model. Once the model is trained, it can be used for inference. Inference is a process of getting a prediction by giving a data point. In this example, we input features of a cat or a dog, and the trained model predicts the output that is a cat or a dog label. The types of machine learning models depend on whether we have a labeled output or not. 04:08 Nikita: Oh, there are different types of machine learning models? Hemant: In general, there are three types of machine learning approaches. In supervised machine learning, labeled data is used to train the model. Model learns the relation between features and labels. Unsupervised learning is generally used to understand relationships within a data set. Labels are not used or are not available. Reinforcement learning uses algorithms that learn from outcomes to make decisions or choices. 04:45 Lois: Ok…supervised learning, unsupervised learning, and reinforcement learning. Where do we use each of these machine learning models? Hemant: Some of the popular applications of supervised machine learning are disease detection, weather forecasting, stock price prediction, spam detection, and credit scoring. For example, in disease detection, the patient data is input to a machine learning model, and machine learning model predicts if a patient is suffering from a disease or not. For unsupervised machine learning, some of the most common real-time applications are to detect fraudulent transactions, customer segmentation, outlier detection, and targeted marketing campaigns. So for example, given the transaction data, we can look for patterns that lead to fraudulent transactions. Most popular among reinforcement learning applications are automated robots, autonomous driving cars, and playing games. 05:51 Nikita: I want to get into how each type of machine learning works. Can we start with supervised learning? Hemant: Supervised learning is a machine learning model that learns from labeled data. The model learns the mapping between the input and the output. As a house price predictor model, we input house size in square feet and model predicts the price of a house. Suppose we need to develop a machine learning model for detecting cancer, the input to the model would be the person's medical details, the output would be whether the tumor is malignant or not. 06:29 Lois: So, that mapping between the input and output is fundamental in supervised learning. Hemant: Supervised learning is similar to a teacher teaching student. The model is trained with the past outcomes and it learns the relationship or mapping between the input and output. In supervised machine learning model, the outputs can be either categorical or continuous. When the output is continuous, we use regression. And when the output is categorical, we use classification. 07:05 Lois: We want to keep this discussion at a high level, so we’re not going to get into regression and classification. But if you want to learn more about these concepts and look at some demonstrations, visit mylearn.oracle.com. Nikita: Yeah, look for the Oracle Cloud Infrastructure AI Foundations course and you’ll find a lot of resources that you can make use of. 07:30 The Oracle University Learning Community is an excellent place to collaborate and learn with Oracle experts and fellow learners. Grow your skills, inspire innovation, and celebrate your successes. All your activities, from liking a post to answering questions and sharing with others, will help you earn a valuable reputation, badges, and ranks to be recognized in the community. Visit to get started. 07:58 Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Hemant? Hemant: Unsupervised machine learning is a type of machine learning where there are no labeled outputs. The algorithm learns the patterns and relationships in the data and groups similar data items. In unsupervised machine learning, the patterns in the data are explored explicitly without being told what to look for. For example, if you give a set of different-colored LEGO pieces to a child and ask to sort it, it may the LEGO pieces based on any patterns they observe. It could be based on same color or same size or same type. Similarly, in unsupervised learning, we group unlabeled data sets. One more example could be-- say, imagine you have a basket of various fruits-- say, apples, bananas, and oranges-- and your task is to group these fruits based on their similarities. You observe that some fruits are round and red, while others are elongated and yellow. Without being told explicitly, you decide to group the round and red fruits together as one cluster and the elongated and yellow fruits as another cluster. There you go. You have just performed an unsupervised learning task. 09:21 Lois: Where is unsupervised machine learning used? Can you take us through some use cases? Hemant: The first use case of unsupervised machine learning is market segmentation. In market segmentation, one example is providing the purchasing details of an online shop to a clustering algorithm. Based on the items purchased and purchasing behavior, the clustering algorithm can identify customers based on the similarity between the products purchased. For example, customers with a particular age group who buy protein diet products can be shown an advertisement of sports-related products. The second use case is on outlier analysis. One typical example for outlier analysis is to provide credit card purchase data for clustering. Fraudulent transactions can be detected by a bank by using outliers. In some transaction, amounts are too high or recurring. It signifies an outlier. The third use case is recommendation systems. An example for recommendation systems is to provide users' movie viewing history as input to a clustering algorithm. It clusters users based on the type or rating of movies they have watched. The output helps to provide personalized movie recommendations to users. The same applies for music recommendations also. 10:53 Lois: And finally, Hemant, let’s talk about reinforcement learning. Hemant: Reinforcement learning is like teaching a dog new tricks. You reward it when it does something right, and over time, it learns to perform these actions to get more rewards. Reinforcement learning is a type of Machine Learning that enables an agent to learn from its interaction with the environment, while receiving feedback in the form of rewards or penalties without any labeled data. Reinforcement learning is more prevalent in our daily lives than we might realize. The development of self-driving cars and autonomous drones rely heavily on reinforcement learning to make real time decisions based on sensor data, traffic conditions, and safety considerations. Many video games, virtual reality experiences, and interactive entertainment use reinforcement learning to create intelligent and challenging computer-controlled opponents. The AI characters in games learn from player interactions and become more difficult to beat as the game progresses. 12:05 Nikita: Hemant, take us through some of the terminology that’s used with reinforcement learning. Hemant: Let us say we want to train a self-driving car to drive on a road and reach its destination. For this, it would need to learn how to steer the car based on what it sees in front through a camera. Car and its intelligence to steer on the road is called as an agent. More formally, agent is a learner or decision maker that interacts with the environment, takes actions, and learns from the feedback received. Environment, in this case, is the road and its surroundings with which the car interacts. More formally, environment is the external system with which the agent interacts. It is the world or context in which the agent operates and receives feedback for its actions. What we see through a camera in front of a car at a moment is a state. State is a representation of the current situation or configuration of the environment at a particular time. It contains the necessary information for the agent to make decisions. The actions in this example are to drive left, or right, or keep straight. Actions are a set of possible moves or decisions that the agent can take in a given state. Actions have an impact on the environment and influence future states. After driving through the road many times, the car learns what action to take when it views a road through the camera. This learning is a policy. Formally, policy is a strategy or mapping that the agent uses to decide which action to take in a given state. It defines the agent's behavior and determines how it selects actions. 13:52 Lois: Ok. Say we’re talking about the training loop of reinforcement learning in the context of training a dog to learn tricks. We want it to pick up a ball, roll, sit… Hemant: Here the dog is an agent, and the place it receives training is the environment. While training the dog, you provide a positive reward signal if the dog picks it right and a warning or punishment if the dog does not pick up a trick. In due course, the dog gets trained by the positive rewards or negative punishments. The same tactics are applied to train a machine in the reinforcement learning. For machines, the policy is the brain of our agent. It is a function that tells what actions to take when in a given state. The goal of reinforcement learning algorithm is to find a policy that will yield a lot of rewards for the agent if the agent follows that policy referred to as the optimal policy. Through a process of learning from experiences and feedback, the agent becomes more proficient at making good decisions and accomplishing tasks. This process continues until eventually we end up with the optimal policy. The optimal policy is learned through training by using algorithms like Deep Q Learning or Q Learning. 15:19 Nikita: So through multiple training iterations, it gets better. That’s fantastic. Thanks, Hemant, for joining us today. We’ve learned so much from you. Lois: Remember, the course and certification are free, so if you’re interested, make sure you log in to mylearn.oracle.com and get going. Join us next week for another episode of the Oracle University Podcast. Until then, I’m Lois Houston… Nikita: And Nikita Abraham signing off! 15:48 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/29721873

Introduction to Artificial Intelligence (AI) 01/30/2024

Introduction to Artificial Intelligence (AI) You probably interact with artificial intelligence (AI) more than you realize. So, there’s never been a better time to start figuring out how it all works. Join Lois Houston and Nikita Abraham as they decode the fundamentals of AI so that anyone, irrespective of their technical background, can leverage the benefits of AI and tap into its infinite potential. Together with Senior Cloud Engineer Nick Commisso, they take you through key AI concepts, common AI tasks and domains, and the primary differences between AI, machine learning, and deep learning. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Welcome to a new season of the Oracle University Podcast. I’m so excited about this season because we’re going to delve into the world of artificial intelligence. In upcoming episodes, we’ll talk about the fundamentals of artificial intelligence and machine learning. And we’ll discuss neural network architectures, generative AI and large language models, the OCI AI stack, and OCI AI services. 01:06 Nikita: So, if you’re an IT professional who wants to start learning about AI and ML or even if you’re a student who is familiar with OCI or similar cloud services, but have no prior exposure to this field, you’ll want to tune in to these episodes. Lois: That’s right, Niki. So, let’s get started. Today, we’ll talk about the basics of artificial intelligence with Senior Cloud Engineer Nick Commisso. Hi Nick! Thanks for joining us today. So, let’s start right at the beginning. What is artificial intelligence? 01:36 Nick: Well, the ability of machines to imitate the cognitive abilities and problem solving capabilities of human intelligence can be classified as artificial intelligence or AI. 01:47 Nikita: Now, when you say capabilities and abilities, what are you referring to? Nick: Human intelligence is the intellectual capability of humans that allows us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using a language and understand the nonverbal cues, such as facial recognition, tone variation, and body language. You can handle objections in real time, even in a complex setting. You can plan for short and long-term situations or projects. And, of course, you can create music and art or invent something new like an original idea. If you can replicate any of these human capabilities in machines, this is artificial general intelligence or AGI. So in other words, AGI can mimic human sensory and motor skills, performance, learning, and intelligence, and use these abilities to carry out complicated tasks without human intervention. When we apply AGI to solve problems with specific and narrow objectives, we call it artificial intelligence or AI. 02:55 Lois: It seems like AI is everywhere, Nick. Can you give us some examples of where AI is used? Nick: AI is all around us, and you've probably interacted with AI, even if you didn't realize it. Some examples of AI can be viewing an image or an object and identifying if that is an apple or an orange. It could be examining an email and classifying it spam or not. It could be writing computer language code or predicting the price of an older car. So let's get into some more specifics of AI tasks and the nature of related data. Machine learning, deep learning, and data science are all associated with AI, and it can be confusing to distinguish. 03:36 Nikita: Why do we need AI? Why’s it important? Nick: AI is vital in today's world, and with the amount of data that's generated, it far exceeds the human ability to absorb, interpret, and actually make decisions based on that data. That's where AI comes in handy by enhancing the speed and effectiveness of human efforts. So here are two major reasons why we need AI. Number one, we want to eliminate or reduce the amount of routine tasks, and businesses have a lot of routine tasks that need to be done in large numbers. So things like approving a credit card or a bank loan, processing an insurance claim, recommending products to customers are just some example of routine tasks that can be handled. And second, we, as humans, need a smart friend who can create stories and poems, designs, create code and music, and have humor, just like us. 04:33 Lois: I’m onboard with getting help from a smart friend! There are different domains in AI, right, Nick? Nick: We have language for language translation; vision, like image classification; speech, like text to speech; product recommendations that can help you cross-sell products; anomaly detection, like detecting fraudulent transactions; learning by reward, like self-driven cars. You have forecasting with weather forecasting. And, of course, generating content like image from text. 05:03 Lois: There are so many applications. Nick, can you tell us more about these commonly used AI domains like language, audio, speech, and vision? Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, or extracting key phrases and so on. Consider the example of translating text. There's many text translation tools where you simply type or paste your text into a given text box, choose your source and target language, and then click translate. Now, let's look at the generative AI tasks. They are generative, which means the output text is generated by a model. Some examples are creating text like stories or poems, summarizing a text, answering questions, and so on. Let's take the example of ChatGPT, the most well-known generative chat bot. These bots can create responses from their training on large language models, and they continuously grow through machine learning. 06:10 Nikita: What can you tell us about using text as data? Nick: Text is inherently sequential, and text consists of sentences. Sentences can have multiple words, and those words need to be converted to numbers for it to be used to train language models. This is called tokenization. Now, the length of sentences can vary, and all the sentences lengths need to be made equal. This is done through padding. Words can have similarities with other words, and sentences can also be similar to other sentences. The similarity can be measured through dot similarity or cosine similarity. We need a way to indicate that similar words or sentences may be close by. This is done through representation called embedding. 06:56 Nikita: And what about language AI models? Nick: Language AI models refer to artificial intelligence models that are specifically designed to understand, process, and generate natural language. These models have been trained on vast amounts of textual data that can perform various natural language processing or NLP tasks. The task that needs to be performed decides the type of input and output. The deep learning model architectures that are typically used to train models that perform language tasks are recurrent neural networks, which processes data sequentially and stores hidden states, long short-term memory, which processes data sequentially that can retain the context better through the use of gates, and transformers, which processes data in parallel. It uses the concept of self-attention to better understand the context. 07:48 Lois: And then there’s speech-related AI, right? Nick: Speech-related AI tasks can be either audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech-to-text conversion or speaker recognition, voice conversion, and so on. Generative AI tasks are generative in nature, so the output audio is generated by a model. For example, you have music composition and speech synthesis. Audio or speech is digitized as snapshots taken in time. The sample rate is the number of times in a second an audio sample is taken. Most digital audio have a sampling rate of 44.1 kilohertz, which is also the sampling rate for audio CDs. Multiple samples need to be correlated to make sense of the data. For example, listening to a song for a fraction of a second, you won't be able to infer much about the song, and you'll probably need to listen to it a little bit longer. Audio and speech AI models are designed to process and understand audio data, including spoken language. These deep-learning model architectures are used to train models that perform language with tasks-- recurrent neural networks, long short-term memory, transformers, variational autoencoders, waveform models, and Siamese networks. All of the models take into consideration the sequential nature of audio. 09:21 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started. 09:49 Nikita: Welcome back! Now that we’ve covered language and speech-related tasks, let’s move on to vision-related tasks. Nick: Vision-related AI tasks could be image related or generative AI. Image-related AI tasks will use an image as an input, and the output depends on the task. Some examples are classifying images, identifying objects in an image, and so on. Facial recognition is one of the most popular image-related tasks that is often used for surveillance and tracking of people in real time, and it's used in a lot of different fields, including security, biometrics, law enforcement, and social media. For generative AI tasks, the output image is generated by a model. For example, creating an image from a contextual description, generating images of a specific style or a high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of an object, machine components, buildings, medication, people, and even more. 10:53 Lois: So, then, here again I need to ask, how do images work as data? Nick: Images consist of pixels, and pixels can be either grayscale or color. And we can't really make out what an image is just by looking at one pixel. The task that needs to be performed decides the type of input needed and the output produced. Various architectures have evolved to handle this wide variety of tasks and data. These deep-learning model architectures are typically used to train models that perform vision tasks-- convolutional neural networks, which detects patterns in images; learning hierarchical representations of visual features; YOLO, which is You Only Look Once, processes the image and detects objects within the image; and then you have generative adversarial networks, which generates real-looking images. 11:43 Nikita: Nick, earlier you mentioned other AI tasks like anomaly detection, recommendations, and forecasting. Could you tell us more about them? Nick: Anomaly detection. This is time-series data, which is required for anomaly detection, and it can be a single or multivariate for fraud detection, machine failure, etc. Recommendations. You can recommend products using data of similar products or users. For recommendations, data of similar products or similar users is required. Forecasting. Time-series data is required for forecasting and can be used for things like weather forecasting and predicting the stock price. 12:22 Lois: Nick, help me understand the difference between artificial intelligence, machine learning, and deep learning. Let’s start with AI. Nick: Imagine a self-driving car that can make decisions like a human driver, such as navigating traffic or detecting pedestrians and making safe lane changes. AI refers to the broader concept of creating machines or systems that can perform tasks that typically require human intelligence. Next, we have machine learning or ML. Visualize a spam email filter that learns to identify and move spam emails to the spam folder, and that's based on the user's interaction and email content. Now, ML is a subset of AI that focuses on the development of algorithms that enable machines to learn from and make predictions or decisions based on data. To understand what an algorithm is in the context of machine learning, it refers to a specific set of rules, mathematical equations, or procedures that the machine learning model follows to learn from data and make predictions on. And finally, we have deep learning or DL. Think of an image recognition software that can identify specific objects or animals within images, such as recognizing cats in photos on the internet. DL is a subfield of ML that uses neural networks with many layers, deep neural networks, to learn and make sense of complex patterns in data. 13:51 Nikita: Are there different types of machine learning? Nick: There are several types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning where the algorithm learns from labeled data, making predictions or classifications. Unsupervised learning is an algorithm that discovers patterns and structures in unlabeled data, such as clustering or dimensionality reduction. And then, you have reinforcement learning, where agents learn to make predictions and decisions by interacting with an environment and receiving rewards or punishments. 14:27 Lois: Can we do a deep dive into each of these types you just mentioned? We can start with the supervised machine learning algorithm. Nick: Let's take an example of how a credit card company would approve a credit card. Once the application and documents are submitted, a verification is done, followed by a credit score check and another 10 to 15 days for approval. And how is this done? Sometimes, purely manually or by using a rules engine where you can build rules, give new data, get a decision. The drawbacks are slow. You need skilled people to build and update rules, and the rules keep changing. The good thing is that the businesses had a lot of insight as to how the decisions were made. Can we build rules by looking at the past data? We all learn by examples. Past data is nothing but a set of examples. Maybe reviewing past credit card approval history can help. Through a process of training, a model can be built that will have a specific intelligence to do a specific task. The heart of training a model is an algorithm that incrementally updates the model by looking at the data samples one by one. And once it's built, the model can be used to predict an outcome on a new data. We can train the algorithm with credit card approval history to decide whether to approve a new credit card. And this is what we call supervised machine learning. It's learning from labeled data. 15:52 Lois: Ok, I see. What about the unsupervised machine learning algorithm? Nick: Data does not have a specific outcome or a label as we know it. And sometimes, we want to discover trends that the data has for potential insights. Similar data can be grouped into clusters. For example, retail marketing and sales, a retail company may collect information like household size, income, location, and occupation so that the suitable clusters could be identified, like a small family or a high spender and so on. And that data can be used for marketing and sales purposes. Regulating streaming services. A streaming service may collect information like viewing sessions, minutes per session, number of unique shows watched, and so on. That can be used to regulate streaming services. Let's look at another example. We all know that fruits and vegetables have different nutritional elements. But do we know which of those fruits and vegetables are similar nutritionally? For that, we'll try to cluster fruits and vegetables' nutritional data and try to get some insights into it. This will help us include nutritionally different fruits and vegetables into our daily diets. Exploring patterns and data and grouping similar data into clusters drives unsupervised machine learning. 17:13 Nikita: And then finally, we come to the reinforcement learning algorithm. Nick: How do we learn to play a game, say, chess? We'll make a move or a decision, check to see if it's the right move or feedback, and we'll keep the outcomes in your memory for the next step you take, which is learning. Reinforcement learning is a machine learning approach where a computer program learns to make decisions by trying different actions and receiving feedback. It teaches agents how to solve tasks by trial and error. This approach is used in autonomous car driving and robots as well. 17:46 Lois: We keep coming across the term “deep learning.” You’ve spoken a bit about it a few times in this episode, but what is deep learning, really? How is it related to machine learning? Nick: Deep learning is all about extracting features and rules from data. Can we identify if an image is a cat or a dog by looking at just one pixel? Can we write rules to identify a cat or a dog in an image? Can the features and rules be extracted from the raw data, in this case, pixels? Deep learning is really useful in this situation. It's a special kind of machine learning that trains super smart computer networks with lots of layers. And these networks can learn things all by themselves from pictures, like figuring out if a picture is a cat or a dog. 18:28 Lois: I know we’re going to be covering this in detail in an upcoming episode, but before we let you go, can you briefly tell us about generative AI? Nick: Generative AI, a subset of machine learning, creates diverse content like text, audio, images, and more. These models, often powered by neural networks, learn patterns from existing data to craft fresh and creative output. For instance, ChatGPT generates text-based responses by understanding patterns in text data that it's been trained on. Generative AI plays a vital role in various AI tasks requiring content creation and innovation. 19:07 Nikita: Thank you, Nick, for sharing your expertise with us. To learn more about AI, go to mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. As you complete the course, you’ll find skill checks that you can attempt to solidify your learning. Lois: And remember, the AI Foundations course on MyLearn also prepares you for the Oracle Cloud Infrastructure 2023 AI Foundations Associate certification. Both the course and the certification are free, so there’s really no reason NOT to take the leap into AI, right Niki? Nikita: That’s right, Lois! Lois: In our next episode, we will look at the fundamentals of machine learning. Until then, this is Lois Houston… Nikita:... /episode/index/show/oracleuniversitypodcast/id/29616933

Everything You Need to Know to Get Certified on Oracle Autonomous Database 01/23/2024

Everything You Need to Know to Get Certified on Oracle Autonomous Database How do I get certified in Oracle Autonomous Database? What material can I use to prepare for it? What's the exam like? How long is the certification valid for? If these questions have been keeping you up at night, then join Lois Houston and Nikita Abraham in their conversation with Senior Principal OCI Instructor Susan Jang to understand the process of getting certified and begin your learning adventure. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! If you’ve listened to us these last few weeks, you’ll know we’ve been discussing Oracle Autonomous Database in detail. We looked at Autonomous Database on serverless and dedicated infrastructure. 00:51 Lois: That’s right, Niki. Then, last week, we explored Autonomous Database tools. Today, we thought we’d wrap up our focus on Autonomous Database by talking about the training offered by Oracle University, the associated certification, how to prepare for it, what you should do next, and more. Nikita: Yeah, we’ll get answers to all the big questions. And we’re going to get them from Susan Jang. Sue is a Senior Principal OCI Instructor with Oracle University. She has created and delivered training in Oracle databases and Oracle Cloud Infrastructure for over 20 years. Hi Sue! Thanks for joining us today. Sue: Happy to be here! 01:29 Lois: Sue, what training does Oracle have on Autonomous Database? Sue: Oracle University offers a professional-level course called the Oracle Autonomous Database Administration Workshop. So, if you want to learn to deploy and administer autonomous databases, this is the one for you. You’ll explore the fundamentals of the autonomous databases, their features, and benefits. You’ll learn about the technical architecture, the tasks that are involved in creating an autonomous database on a shared and on a dedicated Exadata infrastructure. You’ll discover what is the Machine Learning, you’ll discover what is APEX, which is Application Express, and SQL Developer Web, which is all deployed with the Autonomous Database. So basically everything you need to take your skills to the next level and become a proficient database administrator is in this course. 02:28 Nikita: Who can take this course, Sue? Sue: The course is really for anyone interested in Oracle Autonomous Database, whether you’re a database administrator, a cloud data management professional, or a consultant. The topics in the course include everything from the features of an Autonomous Database through provisioning, managing, and monitor of the database. Most people think that just because it is an Autonomous Database, Oracle will do everything for you, and there is nothing a DBA can do or needs to do. But that’s not true. An Oracle Autonomous Database automates the day-to-day DBA tasks, like tuning the database to ensure it is running at performance level or that the backups are done successfully. By letting the Autonomous Database perform those tasks, it gives the database administrator time to fully understand the new features of an Oracle database and figure out how to implement the features that will benefit the DBA’s company. 03:30 Lois: Would a non-database administrator benefit from taking this course? Sue: Yes, Lois. Oracle courses are designed in modules, so you can focus on the modules that meet your needs. For example, if you’re a senior technical manager, you may not need to manage and monitor the Autonomous Database. But still, it’s important to understand its features and architecture to know how other Oracle products integrate with the database. 03:57 Nikita: Right. Talking about the course itself, each module consists of videos that teach different concepts, right? Sue: Yes, Niki. Each video covers one topic. A group of topics, or I should say a group of related topics, makes up a module. We know your time is important to you, and your success is important to us. You don’t just want to spend time taking training. You want to know that you’re really understanding the concepts of what you are learning. So to help you do this, we have skill checks at the end of most modules. You must successfully answer 80% of the questions to pass these knowledge checks. These checks are an excellent way to ensure that you’re on the right track and have the understanding of each module before you move on to the next one. 04:48 Lois: That’s great. And are there any other resources to help reinforce what’s been learned? Sue: I grew up with this phrase from my Mom. Education was her career. I remember hearing, “I hear and I forget. I see and I remember. I do and I understand.” It’s important to us that you understand the concepts and can actually “do” or “perform” the tasks. You'll find several demos in the different modules of the Autonomous Database Administration Workshop. These videos are where the instructor shows you how to perform the tasks so you can reinforce what you learned in the lessons. You’ll find demos on provisioning an autonomous database, creating an autonomous database clone, and configuring disaster recovery, and lots more. Oracle also has what we call LiveLabs. These are a series of hands-on tutorials with step-by-step instructions to guide you through performing the tasks. 05:49 Nikita: I love the idea of LiveLabs. You can follow instructions on how to perform administrative tasks and then practice doing that on your own. Lois: Yeah, that’s fantastic. OK Sue, say I’ve taken the course. What do I do next? Sue: Well, after you’ve taken the course, you’ll want to demonstrate your expertise with a certification. Because you want to get that better job. You want to increase your earning potential. You need to take the certification called the Oracle Autonomous Database Cloud Professional. We have a couple of resources to help you along the way to ensure you succeed in securing that certification. In MyLearn, the Oracle University online learning platform, you’ll see that the course, Oracle Autonomous Database Administration Workshop, falls within a learning path called Become an Oracle Autonomous Database Cloud Professional. The course is the first section of this learning path. The next section is a video describing the certification exam and how to prepare for it. The section after that is a practice exam. Now, though it doesn’t have the actual questions, you’ll find the exam will give you a good idea of the type of questions that will be asked in the exam. 07:10 Lois: OK, so now I’ve done all that, and I’m ready to validate my knowledge and expertise. Tell me more about the certification, Sue. Sue: To get the certification, you must take an online exam. The duration of the exam is 90 minutes. It’s a Multiple Choice format, and there are 60 questions to the exam. By getting this certification, you’re demonstrating to the world that you have the knowledge to provision, manage, and monitor, as well as migrate workloads to the Autonomous Database, on both a shared as well as a dedicated Exadata infrastructure. You will show you have the understanding of the architect of the Autonomous Database and can successfully use the features as well as its workflow, and you are capable of using Autonomous Database tools in developing an Autonomous Database. 08:05 Nikita: Great! So what do I need to do to take the exam? Sue: We assume you’ve already taken the course (making sure that you’re up to date with the training), that you’ve taken the time to study the topics in depth rather than memorizing superficial information just to pass the exam, looked at the available preparation material, and you’ve also taken the practice exam. I highly recommend that you have the hands-on experience or practice on an Autonomous Database before you take the certification exam. 08:38 Nikita: Hold on, Sue. You said to make sure we’re up to date with the training. How do I do that? Sue: Technology is ever-changing, and at Oracle, we continually enhance our products to provide features that make them faster or more straightforward to use. So, if you’re taking a course, you may find a small tag that says “New” next to a topic. That indicates that there are some new training that’s been added to the course. So what I’m trying to say is if you’re looking to take some certification, check the course before you register for the exam and to see if there are any “New” tags. If you find them, you can learn what’s new and not have to go through the entire course again. This way, you’re up to date with the training! 09:25 Nikita: Ok. Got it. Tell us more about the certification, Sue. Sue: If you’re ready, search for the Become An Oracle Autonomous Database Cloud Professional learning path in MyLearn and scroll down to the Oracle Database Cloud Professional exam. Click on the “Register Now” button. You’ll be taken to a page where you’ll see the exam overview, the resources to help you prepare for the exam, a button to register for the exam, and things to do before your exam session. It will also describe what happens after the exam and some exam policies, like what to do if you need to reschedule your exam. When you’re ready to take the exam, you can schedule the date and time according to when it’s convenient for you. 10:15 Lois: What’s the actual experience of taking the exam like? Sue: It’s pretty straightforward. You want to prepare your system a day or two before the exam. You want to ensure you can connect successfully to the test site and that your laptop is plugged in and not running on battery. You want to make sure all other applications are closed before you perform the system test. Now, the system test is really with the test site and consists of testing your microphone, an internet speed test, and your video. You will also be asked to do a test-exam simulation. You will need to be able to download the simulation exam and answer a few simple true or false questions. Once you have successfully done that, you’re ready to take the test on your laptop on the actual day of the test. Now, on the day of the test, set up your test environment. For your test environment, what it really entails is that you have an environment that you do not have anything on your desk. You cannot have a second monitor. And it’s best to have a clear wall behind you so that the proctor can see there is nothing around you. And don’t forget to turn off your mobile device. 11:34 Lois: Ok, I’ve taken the test, and I passed. Wohoo! What happens now? Sue: When you pass the exam, you will receive an email from Oracle with your results as well as a link to Oracle CertView. This is the Oracle certification candidate portal. In CertView, you can download and print your eCertificate. You can share your newly earned badge on places like Facebook, Twitter, and LinkedIn, or even email your employer and others a secure link that they can use to confirm and validate your credentials. 12:11 Nikita: Can anyone take the certification? Sue: Yes, Niki. This certification is available to all candidates, including on-premise database administrators, cloud data management professionals, and consultants. 12:24 Lois: How long is the certification valid? What happens when it expires? Sue: Certain Oracle credentials require periodic recertification for Oracle to recognize them as "active." For such credentials, you must upgrade to a current version within 12 months following the Oracle credential retirement to keep your certification active. 12:51 Are you planning to become an Oracle Certified Professional this year? Whether you're a seasoned IT pro or just starting your career, getting certified can give you a significant boost. And don't worry, we've got your back. Join us at one of our cert prep live events in the Oracle University Learning Community. You'll get insider tips from seasoned experts and learn from other professionals' experiences. Plus, once you've earned your certification, you'll become part of our exclusive forum for Oracle-certified users. So, what are you waiting for? Head over to mylearn.oracle.com and create an account to jump-start your journey towards certification today! 13:35 Nikita: Welcome back. Sue, what other training can I take after Autonomous Database? Sue: Now that you have a strong foundation in the database, there is so much more that you can learn in Oracle. You can consider Exadata if you work on a high-performance data workload that’s running mission-critical applications. Look for a learning path called Become an Exadata Service Cloud Administrator, in MyLearn, to help you with that. GoldenGate is also a good choice if you work with data that needs to be shared and replicated, both locally as well as globally. The course for this is called Oracle GoldenGate 19c: Administration/Implementation. A hot topic in technology today is generative AI (Artificial Intelligence). You want to learn how to implement data security on different levels when it needs to be shared with large language model providers. Perhaps venture beyond the database and learn about Oracle Cloud Infrastructure and how its components and the many cloud services work together. Just go to mylearn.oracle.com, and in the field where you see “What do you want to learn?” type in what interests you and let your learning adventure begin! 14:59 Lois: And since you brought up AI, Sue, this is the perfect time to mention that we’ll be focusing on it for the next couple of weeks. We’ll be speaking to some of our colleagues on topics like artificial intelligence, machine learning, deep learning, generative AI, the OCI AI portfolio and more, but we’ll talk more about that next week. Nikita: Yeah, can’t wait for that. Thank you so much, Sue, for giving us your time today. Sue: Thanks for having me! Lois: Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 15:29 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/29609398

Autonomous Database Tools 01/16/2024

Autonomous Database Tools In this episode, hosts Lois Houston and Nikita Abraham speak with Oracle Database experts about the various tools you can use with Autonomous Database, including Oracle Application Express (APEX), Oracle Machine Learning, and more. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Tamal Chatterjee, and the OU Studio Team for helping us create this episode. --------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! We spent the last two episodes exploring Oracle Autonomous Database’s deployment options: Serverless and Dedicated. Today, it’s tool time! Lois: That’s right, Niki. We’ll be chatting with some of our Database experts on the tools that you can use with the Autonomous Database. We’re going to hear from Patrick Wheeler, Kay Malcolm, Sangeetha Kuppuswamy, and Thea Lazarova. Nikita: First up, we have Patrick, to take us through two important tools. Patrick, let’s start with Oracle Application Express. What is it and how does it help developers? 01:15 Patrick: Oracle Application Express, also known as APEX-- or perhaps APEX, we're flexible like that-- is a low-code development platform that enables you to build scalable, secure, enterprise apps with world-class features that can be deployed anywhere. Using APEX, developers can quickly develop and deploy compelling apps that solve real problems and provide immediate value. You don't need to be an expert in a vast array of technologies to deliver sophisticated solutions. Focus on solving the problem, and let APEX take care of the rest. 01:52 Lois: I love that it’s so easy to use. OK, so how does Oracle APEX integrate with Oracle Database? What are the benefits of using APEX on Autonomous Database? Patrick: Oracle APEX is a fully supported, no-cost feature of Oracle Database. If you have Oracle Database, you already have Oracle APEX. You can access APEX from database actions. Oracle APEX on Autonomous Database provides a preconfigured, fully managed, and secure environment to both develop and deploy world-class applications. Oracle takes care of configuration, tuning, backups, patching, encryption, scaling, and more, leaving you free to focus on solving your business problems. APEX enables your organization to be more agile and develop solutions faster for less cost and with greater consistency. You can adapt to changing requirements with ease, and you can empower professional developers, citizen developers, and everyone else. 02:56 Nikita: So you really don’t need to have a lot of specializations or be an expert to use APEX. That’s so cool! Now, what are the steps involved in creating an application using APEX? Patrick: You will be prompted to log in as the administrator at first. Then, you may create workspaces for your respective users and log in with those associated credentials. Application Express provides you with an easy-to-use, browser-based environment to load data, manage database objects, develop REST interfaces, and build applications which look and run great on both desktop and mobile devices. You can use APEX to develop a wide variety of solutions, import spreadsheets, and develop a single source of truth in minutes. Create compelling data visualizations against your existing data, deploy productivity apps to elegantly solve a business need, or build your next mission-critical data management application. There are no limits on the number of developers or end users for your applications. 04:01 Lois: Patrick, how does APEX use SQL? What role does SQL play in the development of APEX applications? Patrick: APEX embraces SQL. Anything you can express with SQL can be easily employed in an APEX application. Application Express also enables low-code development, providing developers with powerful data management and data visualization components that deliver modern, responsive end user experiences out-of-the-box. Instead of writing code by hand, you're able to use intelligent wizards to guide you through the rapid creation of applications and components. Creating a new application from APEX App Builder is as easy as one, two, three. One, in App Builder, select a project name and appearance. Two, add pages and features to the app. Three, finalize settings, and click Create. 05:00 Nikita: OK. So, the other tool I want to ask you about is Oracle Machine Learning. What can you tell us about it, Patrick? Patrick: Oracle Machine Learning, or OML, is available with Autonomous Database. A new capability that we've introduced with Oracle Machine Learning is called Automatic Machine Learning, or AutoML. Its goal is to increase data scientist productivity while reducing overall compute time. In addition, AutoML enables non-experts to leverage machine learning by not requiring deep understanding of the algorithms and their settings. 05:37 Lois: And what are the key functions of AutoML? Patrick: AutoML consists of three main functions: Algorithm Selection, Feature Selection, and Model Tuning. With Automatic Algorithm Selection, the goal is to identify the in-database algorithms that are likely to achieve the highest model quality. Using metalearning, AutoML leverages machine learning itself to help find the best algorithm faster than with exhaustive search. With Automatic Feature Selection, the goal is to denoise data by eliminating features that don't add value to the model. By identifying the most predicted features and eliminating noise, model accuracy can often be significantly improved with a side benefit of faster model building and scoring. Automatic Model Tuning tunes algorithm hyperparameters, those parameters that determine the behavior of the algorithm, on the provided data. Auto Model Tuning can significantly improve model accuracy while avoiding manual or exhaustive search techniques, which can be costly both in terms of time and compute resources. 06:44 Lois: How does Oracle Machine Learning leverage the capabilities of Autonomous Database? Patrick: With Oracle Machine Learning, the full power of the database is accessible with the tremendous performance of parallel processing available, whether the machine learning algorithm is accessed via native database SQL or with OML4Py through Python or R. 07:07 Nikita: Patrick, talk to us about the Data Insights feature. How does it help analysts uncover hidden patterns and anomalies? Patrick: A feature I wanted to call the electromagnet, but they didn't let me. An analyst's job can often feel like looking for a needle in a haystack. So throw the switch and all that metallic stuff is going to slam up onto that electromagnet. Sure, there are going to be rusty old nails and screws and nuts and bolts, but there are going to be a few needles as well. It's far easier to pick the needles out of these few bits of metal than go rummaging around in a pile of hay, especially if you have allergies. That's more or less how our Insights tool works. Load your data, kick off a query, and grab a cup of coffee. Autonomous Database does all the hard work, scouring through this data looking for hidden patterns, anomalies, and outliers. Essentially, we run some analytic queries that predict expected values. And where the actual values differ significantly from expectation, the tool presents them here. Some of these might be uninteresting or obvious, but some are worthy of further investigation. You get this dashboard of various exceptional data patterns. Drill down on a specific gauge in this dashboard and significant deviations between actual and expected values are highlighted. 08:28 Lois: What a useful feature! Thank you, Patrick. Now, let’s discuss some terms and concepts that are applicable to the Autonomous JSON Database with Kay. Hi Kay, what’s the main focus of the Autonomous JSON Database? How does it support developers in building NoSQL-style applications? Kay: Autonomous Database supports the JavaScript Object Notation, also known as JSON, natively in the database. It supports applications that use the SODA API to store and retrieve JSON data or SQL queries to store and retrieve data stored in JSON-formatted data. Oracle AJD is Oracle ATP, Autonomous Transaction Processing, but it's designed for developing NoSQL-style applications that use JSON documents. You can promote an AJD service to ATP. 09:22 Nikita: What makes the development of NoSQL-style, document-centric applications flexible on AJD? Kay: Development of these NoSQL-style, document-centric applications is particularly flexible because the applications use schemaless data. This lets you quickly react to changing application requirements. There's no need to normalize the data into relational tables and no impediment to changing the data structure or organization at any time, in any way. A JSON document has its own internal structure, but no relation is imposed on separate JSON documents. Nikita: What does AJD do for developers? How does it actually help them? Kay: So Autonomous JSON Database, or AJD, is designed for you, the developer, to allow you to use simple document APIs and develop applications without having to know anything about SQL. That's a win. But at the same time, it does give you the ability to create highly complex SQL-based queries for reporting and analysis purposes. It has built-in binary JSON storage type, which is extremely efficient for searching and for updating. It also provides advanced indexing capabilities on the actual JSON data. It's built on Autonomous Database, so that gives you all of the self-driving capabilities we've been talking about, but you don't need a DBA to look after your database for you. You can do it all yourself. 11:00 Lois: For listeners who may not be familiar with JSON, can you tell us briefly what it is? Kay: So I mentioned this earlier, but it's worth mentioning again. JSON stands for JavaScript Object Notation. It was originally developed as a human readable way of providing information to interchange between different programs. So a JSON document is a set of fields. Each of these fields has a value, and those values can be of various data types. We can have simple strings, we can have integers, we can even have real numbers. We can have Booleans that are true or false. We can have date strings, and we can even have the special value null. Additionally, values can be objects, and objects are effectively whole JSON documents embedded inside a document. And of course, there's no limit on the nesting. You can nest as far as you like. Finally, we can have a raise, and a raise can have a list of scalar data types or a list of objects. 12:13 Nikita: Kay, how does the concept of schema apply to JSON databases? Kay: Now, JSON documents are stored in something that we call collections. Each document may have its own schema, its own layout, to the JSON. So does this mean that JSON document databases are schemaless? Hmmm. Well, yes. But there's nothing to fear because you can always use a check constraint to enforce a schema constraint that you wish to introduce to your JSON data. Lois: Kay, what about indexing capabilities on JSON collections? Kay: You can create indexes on a JSON collection, and those indexes can be of various types, including our flexible search index, which indexes the entire content of the document within the JSON collection, without having to know anything in advance about the schema of those documents. Lois: Thanks Kay! 13:18 AI is being used in nearly every industry—healthcare, manufacturing, retail, customer service, transportation, agriculture, you name it! And, it’s only going to get more prevalent and transformational in the future. So it’s no wonder that AI skills are the most sought after by employers. We’re happy to announce a new OCI AI Foundations certification and course that is available—for FREE! Want to learn about AI? Then this is the best place to start! So, get going! Head over to mylearn.oracle.com to find out more. 13:54 Nikita: Welcome back! Sangeetha, I want to bring you in to talk about Oracle Text. Now I know that Oracle Database is not only a relational store but also a document store. And you can load text and JSON assets along with your relational assets in a single database. When I think about Oracle and databases, SQL development is what immediately comes to mind. So, can you talk a bit about the power of SQL as well as its challenges, especially in schema changes? Sangeetha: Traditionally, Oracle has been all about SQL development. And with SQL development, it's an incredibly powerful language. But it does take some advanced knowledge to make the best of it. So SQL requires you to define your schema up front. And making changes to that schema could be a little tricky and sometimes highly bureaucratic task. In contrast, JSON allows you to develop your schema as you go--the schemaless, perhaps schema-later model. By imposing less rigid requirements on the developer, it allows you to be more fluid and Agile development style. 15:09 Lois: How does Oracle Text use SQL to index, search, and analyze text and documents that are stored in the Oracle Database? Sangeetha: Oracle Text can perform linguistic analyses on documents as well as search text using a variety of strategies, including keyword searching, context queries, Boolean operations, pattern matching, mixed thematic queries, like HTML/XML session searching, and so on. It can also render search results in various formats, including unformatted text, HTML with term highlighting, and original document format. Oracle Text supports multiple languages and uses advanced relevance-ranking technology to improve search quality. Oracle Text also offers advantage features like classification, clustering, and support for information visualization metaphors. Oracle Text is now enabled automatically in Autonomous Database. It provides full-text search capabilities over text, XML, JSON content. It also could extend current applications to make better use of textual fields. It builds new applications specifically targeted at document searching. Now, all of the power of Oracle Database and a familiar development environment, rock-solid autonomous database infrastructure for your text apps, we can deal with text in many different places and many different types of text. So it is not just in the database. We can deal with data that's outside of the database as well. 17:03 Nikita: How does it handle text in various places and formats, both inside and outside the database? Sangeetha: So in the database, we can be looking a varchar2 column or LOB column or binary LOB columns if we are talking about binary documents such as PDF or Word. Outside of the database, we might have a document on the file system or out on the web with URLs pointing out to the document. If they are on the file system, then we would have a file name stored in the database table. And if they are on the web, then we should have a URL or a partial URL stored in the database. And we can then fetch the data from the locations and index it in the term documents format. We recognize many different document formats and extract the text from them automatically. So the basic forms we can deal with-- plain text, HTML, JSON, XML, and then formatted documents like Word docs, PDF documents, PowerPoint documents, and also so many different types of documents. All of those are automatically handled by the system and then processed into the format indexing. And we are not restricted by the English either here. There are various stages in the index pipeline. A document starts one, and it's taken through the different stages so until it finally reaches the index. 18:44 Lois: You mentioned the indexing pipeline. Can you take us through it? Sangeetha: So it starts with a data store. That's responsible for actually reaching the document. So once we fetch the document from the data store, we pass it on to the filter. And now the filter is responsible for processing binary documents into indexable text. So if you have a PDF, let's say a PDF document, that will go through the filter. And that will extract any images and return it into the stream of HTML text ready for indexing. Then we pass it on to the sectioner, which is responsible for identifying things like paragraphs and sentences. The output from the section is fed onto the lexer. The lexer is responsible for dividing the text into indexable words. The output of the lexer is fed into the index engine, which is responsible for laying out to the indexes on the disk. Storage, word list, and stop list are some additional inputs there. So storage tells exactly how to lay out the index on disk. Word list which has special preferences like desegmentation. And then stop is a list word that we don't want to index. So each of these stages and inputs can be customized. Oracle has something known as the extensibility framework, which originally was designed to allow people to extend capabilities of these products by adding new domain indexes. And this is what we've used to implement Oracle Text. So when kernel sees this phrase INDEXTYPE ctxsys.context, it knows to handle all of the hard work creating the index. 20:48 Nikita: Other than text indexing, Oracle Text offers additional operations, right? Can you share some examples of these operations? Sangeetha: So beyond the text index, other operations that we can do with the Oracle Text, some of which are search related. And some examples of that are these highlighting markups and snippets. Highlighting and markup are very similar. They are ways of fetching these results back with the search. And then it's marked up with highlighting within the document text. Snippet is very similar, but it's only bringing back the relevant chunks from the document that we are searching for. So rather than getting the whole document back to you, just get a few lines showing this in a context and the theme and extraction. So Oracle Text is capable of figuring out what a text is all about. We have a very large knowledge base of the English language, which will allow you to understand the concepts and the themes in the document. Then there's entity extraction, which is the ability to find out people, places, dates, times, zip codes, et cetera in the text. So this can be customized with your own user dictionary and your own user rules. 22:14 Lois: Moving on to advanced functionalities, how does Oracle Text utilize machine learning algorithms for document classification? And what are the key types of classifications? Sangeetha: The text analytics uses machine learning algorithms for document classification. We can process a large set of data documents in a very efficient manner using Oracle's own machine learning algorithms. So you can look at that as basically three different headings. First of all, there's classification. And that comes in two different types-- supervised and unsupervised. The supervised classification which means in this classification that it provides the training set, a set of documents that have already defined particular characteristics that you're looking for. And then there's unsupervised classification, which allows your system itself to figure out which documents are similar to each other. It does that by looking at features within the documents. And each of those features are represented as a dimension... /episode/index/show/oracleuniversitypodcast/id/29406983

Autonomous Database on Dedicated Infrastructure 01/09/2024

Autonomous Database on Dedicated Infrastructure The Oracle Autonomous Database Dedicated deployment is a good choice for customers who want to implement a private database cloud in their own dedicated Exadata infrastructure. That dedicated infrastructure can either be in the Oracle Public Cloud or in the customer's own data center via Oracle Exadata Cloud@Customer. In a dedicated environment, the Exadata infrastructure is entirely dedicated to the subscribing customer, isolated from other cloud tenants, with no shared processor, storage, and memory resource. In this episode, hosts Lois Houston and Nikita Abraham speak with Oracle Database experts about how Autonomous Database Dedicated offers greater control of the software and infrastructure life cycle, customizable policies for separation of database workload, software update schedules and versioning, workload consolidation, availability policies, and much more. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Tamal Chatterjee, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and I’m joined by Lois Houston, Director of Innovation Programs. Lois: Hi there! This is our second episode on Oracle’s Autonomous Database, and today we’re going to spend time discussing Autonomous Database on Dedicated Infrastructure. We’ll be talking with three of our colleagues: Maria Colgan, Kamryn Vinson, and Kay Malcolm. 00:53 Nikita: Maria is a Distinguished Product Manager for Oracle Database, Kamryn is a Database Product Manager, and Kay is a Senior Director of Database Product Management. Lois: Hi Maria! Thanks for joining us today. We know that Oracle Autonomous Database offers two deployment choices: serverless and dedicated Exadata infrastructure. We spoke about serverless infrastructure last week but for anyone who missed that episode, can you give us a quick recap of what it is? 01:22 Maria: With Autonomous Database Serverless, Oracle automates all aspects of the infrastructure and database management for you. That includes provisioning, configuring, monitoring, backing up, and tuning. You simply select what type of database you want, maybe a data warehouse, transaction processing, or a JSON document store, which region in the Oracle Public Cloud you want that database deployed, and the base compute and storage resources necessary. Oracle automatically takes care of everything else. Once provisioned, the database can be instantly scaled through our UI, our APIs, or automatically based on your workload needs. All scaling activities happen completely online while the database remains open for business. 02:11 Nikita: Ok, so now that we know what serverless is, let’s move on to dedicated infrastructure. What can you tell us about it? Maria: Autonomous Database Dedicated allows customers to implement a private database cloud running on their own dedicated Exadata infrastructure. That dedicated infrastructure can be in Oracle’s Public Cloud or in the customer's own data center via Oracle Exadata Cloud@Customer. It makes an ideal platform to consolidate multiple databases regardless of their workload type or their size. And it also allows you to offer database as a service within your enterprise. 02:50 Lois: What are the primary benefits of Autonomous Database Dedicated infrastructure? Maria: With the dedicated deployment option, you must first subscribe to Dedicated Exadata Cloud Infrastructure that is isolated from other tenants with no shared processors, memory, network, or storage resources. This infrastructure choice offers greater control of both the software and the infrastructure life cycle. Customers can specify their own policies for workload separation, software update schedules, and availability. One of the key benefits of an autonomous database is a lower total cost of ownership through more automation and operational delegation to Oracle. Remember it’s a fully managed service. All database operations, such as backup, software updates, upgrades, OS maintenance, incident management, and health monitoring, will be automatically done for you by Oracle. Its maximum availability architecture protects you from any hardware failures and in the event of a full outage, the service will be automatically failed over to your standby site. Built-in application continuity ensures zero downtime during the standard software update or in the event of a failover. 04:09 Nikita: And how is this billed? Maria: Autonomous Database also has true pay-per-use billing so even when autoscale is enabled, you’ll only pay for those additional resources when you use them. And we make it incredibly simple to develop on this environment with managed developer add-ons like our low code development environment, APEX, and our REST data services. This means you don’t need any additional development environments in order to get started with a new application. 04:40 Lois: Ok. So, it looks like the dedicated option offers more control and customization. Maria, how do we access a dedicated database over a network? Maria: The network path is through a VCN, or Virtual Cloud Network, and the subnet that's defined by the Exadata infrastructure hosting the database. By default, this subnet is defined as private, meaning, there's no public internet access to those databases. This ensures only your company can access your Exadata infrastructure and your databases. Autonomous Database Dedicated can also take advantage of network services provided by OCI, including subnets or VCN peering, as well as connections to on-prem databases through the IP secure VPN and FastConnect dedicated corporate network connections. 05:33 Maria: You can also take advantage of the Oracle Microsoft partnership that enables customers to connect their Oracle Cloud Infrastructure resources and Microsoft Azure resources through a dedicated private connection. However, for some customers, a move to the public cloud is just not possible. Perhaps it's due to industry regulations, performance concerns, or integration with legacy on-prem applications. For these types of customers, Exadata Cloud@Customer should meet their requirements for strict data sovereignty and security by delivering high-performance Exadata Cloud Services capabilities in their data center behind their own firewall. 06:16 Nikita: What are the benefits of Autonomous Database on Exadata Cloud@Customer? How’s it different? Maria: Autonomous Database on Exadata Cloud@Customer provides the same service as Autonomous Database Dedicated in the public cloud. So you get the same simplicity, agility, and performance, and elasticity that you get in the cloud. But it also provides a very fast and simple transition to an autonomous cloud because you can easily migrate on-prem databases to Exadata Cloud@Customer. Once the database is migrated, any existing applications can simply reconnect to that new database and run without any application changes being needed. And the data will leave your data center, so making it a very safe way to adopt a cloud model. 07:04 Lois: So, how do we manage communication to and from the public cloud? Maria: Each Cloud@Customer rack includes two local control plane servers to manage the communication to and from the public cloud. The local control plane acts on behalf of requests from the public cloud, keeping communications consolidated and secure. Platform control plane commands are sent to the Exadata Cloud@Customer system through a dedicated WebSocket secure tunnel. Oracle Cloud operations staff use that same tunnel to monitor the autonomous database on Exadata Cloud@Customer both for maintenance and for troubleshooting. The two remote, control plane servers installed in the Exadata Cloud@Customer rack host that secure tunnel endpoint and act as a gateway for access to the infrastructure. They also host components that orchestrate the cloud automation, aggregates and routes telemetry messages from the Exadata Cloud@Customer platform to the Oracle Support Service infrastructure. And they also host images for server patching. 08:13 Maria: The Exadata Database Server is connected to the customer-managed switches via either 10 gigabit or 25 gigabit Ethernet. Customers have access to the customer Virtual Machine, or VM, via a pair of layer 2 network connections that are implemented as Virtual Network Interface Cards, or vNICs. They're also tagged VLAN. The physical network connections are implemented for high availability in an active standby configuration. Autonomous Database on Exadata Cloud@Customer provides the best of both worlds-- all of the automation including patching, backing up, scaling, and management of a database that you get with a cloud service, but without the data ever leaving the customer's data center. 09:01 Nikita: That's interesting. And, what happens if a dedicated database loses network connectivity to the OCI control plane? Maria: In the event an autonomous database on Exadata Cloud@Customer loses network connectivity to the OCI control plane, the Autonomous Database will actually continue to be available for your applications. And operations such as backups and autoscaling will not be impacted in that loss of network connectivity. However, the management and monitoring of the Autonomous Database via the OCI console and APIs as well as access by the Oracle Cloud operations team will not be available until that network is reconnected. 09:43 Maria: The capability suspended in the case of a lost network connection include, as I said, infrastructure management-- so that's the manual scaling of an Autonomous Database via the UI or our OCI CLI, or REST APIs, as well as Terraform scripts. They won't be available. Neither will the ability for Oracle Cloud ops to access and perform maintenance activities, such as patching. Nor will we be able to monitor the Oracle infrastructure during the time where the system is not connected. 10:20 Lois: That’s good to know, Maria. What about data encryption and backup options? Maria: All Oracle Autonomous Databases encrypt data at REST. Data is automatically encrypted as it's written to the storage. But this encryption is transparent to authorized users and applications because the database automatically decrypts the data when it's being read from the storage. There are several options for backing up the Autonomous Database Cloud@Customer including using a Zero Data Loss Recovery Appliance, or ZDLRA. You can back it up to locally mounted NFS storage or back it up to the Oracle Public Cloud. 10:57 Nikita: I want to ask you about the typical workflow for Autonomous Database Dedicated infrastructure. What are the main steps here? Maria: In the typical workflow, the fleet administrator role performs the following steps. They provision the Exadata infrastructure by specifying its size, availability domain, and region within the Oracle Cloud. Once the hardware has been provisioned, the fleet administrator partitions the system by provisioning clusters and container databases. Then the developers, DBAs, or anyone who needs a database can provision databases within those container databases. Billing is based on the size of the Exadata infrastructure that's provisioned. So whether that's a quarter rack, half rack, or full rack. It also depends on the number of CPUs that are being consumed. Remember, it's also possible for customers to use their existing Oracle database licenses with this service to reduce the cost. 11:53 Lois: And what Exadata infrastructure models and shapes does Autonomous Database Dedicated support? Maria: That's the X7, X8, and X8M and you can get all of those in either a quarter, half, or full Exadata rack. Currently, you can create a maximum of 12 VM clusters on an Autonomous Database Dedicated infrastructure. We also advise that you limit the number of databases you provision to meet your preferred SLA. To meet the high availability SLA, we recommend a maximum of 100 databases. To meet the extreme availability SLA, we recommend a maximum of 25 databases. 12:35 Nikita: Ok, so now that I know all this, how do I actually get started with Autonomous Database on dedicated infrastructure? Maria: You need to increase your service limit to include that Exadata infrastructure and then you need to create the fleet and DBA service roles. You also need to create the necessary network model, VM clusters, and container databases for your organization. Finally, you need to provide access to the end users who want to create and use those Autonomous databases. Autonomous Database requires a subscription to that Exadata infrastructure for a minimum of 48 hours. But once subscribed, you can test out ideas and then terminate the subscription with no ongoing costs. While subscribed, you can control where you place the resources to perhaps manage latency sensitive applications. 13:29 Maria: You can also have control over patching schedules, software versions, so you can be sure that you're testing exactly what you need to. You can also migrate databases to the Autonomous Database via our export, import capabilities via the object store or through Data Pump or Golden Gate. As with any Autonomous Database, once it's provisioned, you've got full access to both autoscaling and all our cloning capabilities. 13:57 Lois: Maria, I've heard you talk about the importance of clean role separation in managing a private cloud. Can you elaborate on that, please? Maria: A successful private cloud is set up and managed using clean role separation between the fleet administration group and the developers, or DBA groups. The fleet administration group establishes the governance constraints, including things like budgeting, capacity compliance, and SLAs, according to the business structure. The physical resources are also logically grouped to align with this business structure, and then groups of users are given self-service access to the resources within these groups. So a good example of this would be that the developers and DBA groups use self-service database resources within these constraints. 14:46 Nikita: I see. So, what exactly does a fleet administrator do? Maria: Fleet administrators allocate budget by department and are responsible for the creation, monitoring, and management of the autonomous exadata infrastructure, the autonomous exadata VM clusters, and the autonomous container databases. To perform these duties, the fleet administrators must have an Oracle Cloud account or user, and that user must have permissions to manage these resources and be permitted to use network resources that need to be specified when you create these other resources. 15:24 Nikita: And what about database administrators? Maria: Database administrators create, monitor, and manage autonomous databases. They, too, need to have an Oracle Cloud account or be an Oracle Cloud user. Now, those accounts need to have the necessary permissions in order to create and access databases. They also need to be able to access autonomous backups and have permission to access the autonomous container databases, inside which these autonomous databases will be created, and have all of the necessary permissions to be able to create those databases, as I said. While creating autonomous databases, the database administrators will define and gain access to an admin user account inside the database. It's through this account that they will actually get the necessary permissions to be able to create and control database users. 16:24 Lois: How do developers fit into the picture? Maria: Database users and developers who write applications that will use or access an autonomous database don't actually need Oracle Cloud accounts. They'll actually be given the network connectivity and authorization information they need to access those databases by the database administrators. 16:45 Lois: Maria, you mentioned the various ways to manage the lifecycle of an autonomous dedicated service. Can you tell us more about that? Maria: You can manage the lifecycle of an autonomous dedicated service through the Cloud UI, Command Line Interface, through our REST APIs, or through one of the several language SDKs. The lifecycle operations that you can manage include capacity planning and setup, the provisioning and partitioning of exadata infrastructure, the provisioning and management of databases, the scaling of CPU storage and other resources, the scheduling of updates for the infrastructure, the VMs, and the database, as well as monitoring through event notifications. 17:30 Lois: And how do policies come into play? Maria: OCI allows fine-grained control over resources through the application of policies to groups. These policies are applicable to any member of the group. For Oracle Autonomous Database on dedicated infrastructure, the resources in question are autonomous exadata infrastructure, autonomous container databases, autonomous databases, and autonomous backups. Lois: Thanks so much, Maria. That was great information. 18:05 The Oracle University Learning Community is a great place for you to collaborate and learn with experts, peers, and practitioners. Grow your skills, inspire innovation, and celebrate your successes. The more you participate, the more recognition you can earn. All of your activities, from liking a post to answering questions and sharing with others, will help you earn badges and ranks, and be recognized within the community. If you are already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 18:44 Nikita: Welcome back! Hi Kamryn, thanks for joining us on the podcast. So, in an Autonomous Database environment where most DBA tasks are automated, what exactly does an application DBA do? Kamryn: While Autonomous Database automates most of the repetitive tasks that DBAs perform, the application DBA will still want to monitor and diagnose databases for applications to maintain the highest performance and the greatest security possible. Tasks the application DBA performs includes operations on databases, cloning, movement, monitoring, and creating alerts. When required, the application DBA performs low-level diagnostics for application performance and looks for insights on performance and capacity trends. 19:36 Nikita: I see. And which tools do they use for these tasks? Kamryn: There are several tools at the application DBA's disposal, including Enterprise Manager, Performance Hub, and the OCI Console. For Autonomous Dedicated, all the database operations are exposed through the console UI and available through REST API calls, including provisioning, stop/start, lifecycle operations for dedicated database types, unscheduled on-demand backups and restores, CPU scaling and storage management, providing connectivity information, including wallets, scheduling updates. 20:17 Lois: So, Kamryn, what tools can DBAs use for deeper exploration? Kamryn: For deeper exploration of the databases themselves, Autonomous Database DBAs can use SQL Developer Web, Performance Hub, and Enterprise Manager. 20:31 Nikita: Let’s bring Kay into the conversation. Hi Kay! With Autonomous Database Dedicated, I’ve heard that customers have more control over patching. Can you tell us a little more about that? Kay: With Autonomous... /episode/index/show/oracleuniversitypodcast/id/29089378

Autonomous Database on Serverless Infrastructure 01/02/2024

Autonomous Database on Serverless Infrastructure Want to quickly provision your autonomous database? Then look no further than Oracle Autonomous Database Serverless, one of the two deployment choices offered by Oracle Autonomous Database. Autonomous Database Serverless delegates all operational decisions to Oracle, providing you with a completely autonomous experience. Join hosts Lois Houston and Nikita Abraham, along with Oracle Database experts, as they discuss how serverless infrastructure eliminates the need to configure any hardware or install any software because Autonomous Database handles provisioning the database, backing it up, patching and upgrading it, and growing or shrinking it for you. Oracle Autonomous Database Episode: Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Rajeev Grover, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! Welcome back to a new season of the Oracle University Podcast. This time, our focus is going to be on Oracle Autonomous Database. We’ve got a jam-packed season planned with some very special guests joining us. 00:52 Lois: If you’re a regular listener of the podcast, you’ll remember that we’d spoken a bit about Autonomous Database last year. That was a really good introductory episode so if you missed it, you might want to check it out. Nikita: Yeah, we’ll post a link to the episode in today’s show notes so you can find it easily. 01:07 Lois: Right, Niki. So, for today’s episode, we wanted to focus on Autonomous Database on Serverless Infrastructure and we reached out to three experts in the field: Hannah Nguyen, Sean Stacey, and Kay Malcolm. Hannah is an Associate Cloud Engineer, Sean, a Director of Platform Technology Solutions, and Kay, who’s been on the podcast before, is Senior Director of Database Product Management. For this episode, we’ll be sharing portions of our conversations with them. So, let’s get started. 01:38 Nikita: Hi Hannah! How does Oracle Cloud handle the process of provisioning an Autonomous Database? Hannah: The Oracle Cloud automates the process of provisioning an Autonomous Database, and it automatically provisions for you a highly scalable, highly secure, and a highly available database very simply out of the box. 01:56 Lois: Hannah, what are the components and architecture involved when provisioning an Autonomous Database in Oracle Cloud? Hannah: Provisioning the database involves very few steps. But it's important to understand the components that are part of the provisioned environment. When provisioning a database, the number of CPUs in increments of 1 for serverless, storage in increments of 1 terabyte, and backup are automatically provisioned and enabled in the database. In the background, an Oracle 19c pluggable database is being added to the container database that manages all the user's Autonomous Databases. Because this Autonomous Database runs on Exadata systems, Real Application Clusters is also provisioned in the background to support the on-demand CPU scalability of the service. This is transparent to the user and administrator of the service. But be aware it is there. 02:49 Nikita: Ok…So, what sort of flexibility does the Autonomous Database provide when it comes to managing resource usage and costs, you know… especially in terms of starting, stopping, and scaling instances? Hannah: The Autonomous Database allows you to start your instance very rapidly on demand. It also allows you to stop your instance on demand as well to conserve resources and to pause billing. Do be aware that when you do pause billing, you will not be charged for any CPU cycles because your instance will be stopped. However, you'll still be incurring charges for your monthly billing for your storage. In addition to allowing you to start and stop your instance on demand, it's also possible to scale your database instance on demand as well. All of this can be done very easily using the Database Cloud Console. 03:36 Lois: What about scaling in the Autonomous Database? Hannah: So you can scale up your OCPUs without touching your storage and scale it back down, and you can do the same with your storage. In addition to that, you can also set up autoscaling. So the database, whenever it detects the need, will automatically scale up to three times the base level number of OCPUs that you have allocated or provisioned for the Autonomous Database. 04:00 Nikita: Is autoscaling available for all tiers? Hannah: Autoscaling is not available for an always free database, but it is enabled by default for other tiered environments. Changing the setting does not require downtime. So this can also be set dynamically. One of the advantages of autoscaling is cost because you're billed based on the average number of OCPUs consumed during an hour. 04:23 Lois: Thanks, Hannah! Now, let’s bring Sean into the conversation. Hey Sean, I want to talk about moving an autonomous database resource. When or why would I need to move an autonomous database resource from one compartment to another? Sean: There may be a business requirement where you need to move an autonomous database resource, serverless resource, from one compartment to another. Perhaps, there's a different subnet that you would like to move that autonomous database to, or perhaps there's some business applications that are within or accessible or available in that other compartment that you wish to move your autonomous database to take advantage of. 04:58 Nikita: And how simple is this process of moving an autonomous database from one compartment to another? What happens to the backups during this transition? Sean: The way you can do this is simply to take an autonomous database and move it from compartment A to compartment B. And when you do so, the backups, or the automatic backups that are associated with that autonomous database, will be moved with that autonomous database as well. 05:21 Lois: Is there anything that I need to keep in mind when I’m moving an autonomous database between compartments? Sean: A couple of things to be aware of when doing this is, first of all, you must have the appropriate privileges in that compartment in order to move that autonomous database both from the source compartment to the target compartment. In addition to that, once the autonomous database is moved to this new compartment, any policies or anything that's defined in that compartment to govern the authorization and privileges of that said user in that compartment will be applied immediately to that new autonomous database that has been moved into that new compartment. 05:59 Nikita: Sean, I want to ask you about cloning in Autonomous Database. What are the different types of clones that can be created? Sean: It's possible to create a new Autonomous Database as a clone of an existing Autonomous Database. This can be done as a full copy of that existing Autonomous Database, or it can be done as a metadata copy, where the objects and tables are cloned, but they are empty. So there's no rows in the tables. And this clone can be taken from a live running Autonomous Database or even from a backup. So you can take a backup and clone that to a completely new database. 06:35 Lois: But why would you clone in the first place? What are the benefits of this? Sean: When cloning or when creating this clone, it can be created in a completely new compartment from where the source Autonomous Database was originally located. So it's a nice way of moving one database to another compartment to allow developers or another community of users to have access to that environment. 06:58 Nikita: I know that along with having a full clone, you can also have a refreshable clone. Can you tell us more about that? Who is responsible for this? Sean: It's possible to create a refreshable clone from an Autonomous Database. And this is one that would be synced with that source database up to so many days. The task of keeping that refreshable clone in sync with that source database rests upon the shoulders of the administrator. The administrator is the person who is responsible for performing that sync operation. Now, actually performing the operation is very simple, it's point and click. And it's an automated process from the database console. And also be aware that refreshable clones can trail the source database or source Autonomous Database up to seven days. After that period of time, the refreshable clone, if it has not been refreshed or kept in sync with that source database, it will become a standalone, read-only copy of that original source database. 08:00 Nikita: Ok Sean, so if you had to give us the key takeaways on cloning an Autonomous Database, what would they be? Sean: It's very easy and a lot of flexibility when it comes to cloning an Autonomous Database. We have different models that you can take from a live running database instance with zero impact on your workload or from a backup. It can be a full copy, or it can be a metadata copy, as well as a refreshable, read-only clone of a source database. 08:33 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all of which is available free to subscribers. So, get going! Pick a course of your choice, get certified, join the Oracle University Learning Community, and network with your peers. If you are already an Oracle MyLearn user, go to MyLearn to begin your journey. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 09:12 Nikita: Welcome back! Thank you, Sean, and hi Kay! I want to ask you about events and notifications in Autonomous Database. Where do they really come in handy? Kay: Events can be used for a variety of notifications, including admin password expiration, ADB services going down, and wallet expiration warnings. There's this service, and it's called the notifications service. It's part of OCI. And this service provides you with the ability to broadcast messages to distributed components using a publish and subscribe model. These notifications can be used to notify you when event rules or alarms are triggered or simply to directly publish a message. In addition to this, there's also something that's called a topic. This is a communication channel for sending messages to subscribers in the topic. You can manage these topics and their subscriptions really easy. It's not hard to do at all. 10:14 Lois: Kay, I want to ask you about backing up Autonomous Databases. How does Autonomous Database handle backups? Kay: Autonomous Database automatically backs up your database for you. The retention period for backups is 60 days. You can restore and recover your database to any point in time during this retention period. You can initiate recovery for your Autonomous Database by using the cloud console or an API call. Autonomous Database automatically restores and recovers your database to the point in time that you specify. In addition to a point in time recovery, we can also perform a restore from a specific backup set. 10:59 Lois: Kay, you spoke about automatic backups, but what about manual backups? Kay: You can do manual backups using the cloud console, for example, if you want to take a backup say before a major change to make restoring and recovery faster. These manual backups are put in your cloud object storage bucket. 11:20 Nikita: Are there any special instructions that we need to follow when configuring a manual backup? Kay: The manual backup configuration tasks are a one-time operation. Once this is configured, you can go ahead, trigger your manual backup any time you wish after that. When creating the object storage bucket for the manual backups, it is really important-- so I don't want you to forget-- that the name format for the bucket and the object storage follows this naming convention. It should be backup underscore database name. And it's not the display name here when I say database name. 12:00 Kay: In addition to that, the object name has to be all lowercase. So three rules. Backup underscore database name, and the specific database name is not the display name. It has to be in lowercase. Once you've created your object storage bucket to meet these rules, you then go ahead and set a database property. Default_backup_bucket. This points to the object storage URL and it's using the Swift protocol. Once you've got your object storage bucket mapped and you've created your mapping to the object storage location, you then need to go ahead and create a database credential inside your database. You may have already had this in place for other purposes, like maybe you were loading data, you were using Data Pump, et cetera. If you don't, you would need to create this specifically for your manual backups. Once you've done so, you can then go ahead and set your property to that default credential that you created. So once you follow these steps as I pointed out, you only have to do it one time. Once it's configured, you can go ahead and use it from now on for your manual backups. 13:21 Lois: Kay, the last topic I want to talk about before we let you go is Autonomous Data Guard. Can you tell us about it? Kay: Autonomous Data Guard monitors the primary database, in other words, the database that you're using right now. Lois: So, if ADB goes down… Kay: Then the standby instance will automatically become the primary instance. There's no manual intervention required. So failover from the primary database to that standby database I mentioned, it's completely seamless and it doesn't require any additional wallets to be downloaded or any new URLs to access APEX or Oracle Machine Learning. Even Oracle REST Data Services. All the URLs and all the wallets, everything that you need to authenticate, to connect to your database, they all remain the same for you if you have to failover to your standby database. 14:19 Lois: And what happens after a failover occurs? Kay: After performing a failover, a new standby for your primary will automatically be provisioned. So in other words, in performing a failover your standby does become your new primary. Any new standby is made for that primary. I know, it's kind of interesting. So currently, the standby database is created in the same region as the primary database. For better resilience, if your database is provisioned, it would be available on AD1 or Availability Domain 1. My secondary, or my standby, would be provisioned on a different availability domain. 15:10 Nikita: But there’s also the possibility of manual failover, right? What are the differences between automatic and manual failover scenarios? When would you recommend using each? Kay: So in the case of the automatic failover scenario following a disastrous situation, if the primary ADB becomes completely unavailable, the switchover button will turn to a failover button. Because remember, this is a disaster. Automatic failover is automatically triggered. There's no user action required. So if you're asleep and something happens, you're protected. There's no user action required, but automatic failover is allowed to succeed only when no data loss will occur. 15:57 Nikita: For manual failover scenarios in the rare case when an automatic failover is unsuccessful, the switchover button will become a failover button and the user can trigger a manual failover should they wish to do so. The system automatically recovers as much data as possible, minimizing any potential data loss. But you can see anywhere from a few seconds or minutes of data loss. Now, you should only perform a manual failover in a true disaster scenario, expecting the fact that a few minutes of potential data loss could occur, to ensure that your database is back online as soon as possible. 16:44 Lois: Thank you so much, Kay. This conversation has been so educational for us. And thank you once again to Hannah and Sean. To learn more about Autonomous Database, head over to mylearn.oracle.com and search for the Oracle Autonomous Database Administration Workshop. Nikita: Thanks for joining us today. In our next episode, we will discuss Autonomous Database on Dedicated Infrastructure. Until then, this is Nikita Abraham… Lois: …and Lois Houston signing off. 17:12 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/29089433

Best of 2023: Getting Started with Oracle Database 12/26/2023

Best of 2023: Getting Started with Oracle Database In today’s digital economy, data is a form of capital. Given the mission-critical role that it has, having a robust data management strategy is now more crucial than ever. Join Lois Houston and Nikita Abraham, along with Kay Malcolm, as they talk about the various Oracle Database offerings and discuss how to actually use them to efficiently manage data across a diverse but unified data tier. Oracle MyLearn: Oracle University Learning Community: X (formerly Twitter): LinkedIn: Special thanks to Arijit Ghosh, David Wright, Ranbir Singh, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi there. If you’ve been following along with us these past few weeks, you’ll know we’ve been revisiting our most popular episodes of the year. Lois: Right, and today’s episode is the last one of the Best of 2023 series. It’s a throwback to our conversation on Oracle’s Data Management strategy and offerings with Kay Malcolm, Senior Director of Database Product Management at Oracle. Nikita: We’d often heard Kay say that Oracle’s data management strategy is simply complete and completely simple. And so we began by asking her what she meant by that. 01:09 Kay: It's a fun play on words, right? App development paradigms are in a rapid state of transformation. Modern app development is simplifying and accelerating how you deploy applications. Also simplifying how data models and data analytics are used. Oracle data management embraces modern app development and transformations that go beyond technology changes. It presents a simply complete solution that is completely simple. Immediately you can see benefits of the easiest and most productive platform for developing and running modern app and analytics. 01:54 Kay: Oracle Database is a converged database that provides best of breed support for all different data models and workloads that you need. When you have converged support for application development, you eliminate data fragmentation. You can perform unique queries and transactions that span any data and create value across all data types and build into your applications. 02:24 Nikita: When you say all data types, this can include both structured and unstructured data, right? Kay: This also includes structured and unstructured data. The Oracle converged database has the best of breed for JSON, graph, and text while including other data types, relations, blockchain, spatial, and others. Now that we have the ability to access any data type, we have various workloads and converged data management that supports all modern transactional and analytical workloads. We have the unique ability to run any combination of workloads on any combination of data. Simply complete for analytics means the ability to include all of the transactions, including key value, IoT, or Internet of Things, along with operational data warehouse and lake and machine learning. 03:27 Kay: Oracle's decentralized database architecture makes decentralized apps simple to deploy and operate. This architecture makes it simple to use decentralized app development techniques like coding events, data events, API driven development, low code, and geo distribution. Autonomous Database or ADB now supports the Mongo database API adding more tools for architectural support. Autonomous Database or ADB has a set of automated tools to manage, provision, tune, and patch. It provides solutions for difficult database engineering with auto indexing and partitioning and is elastic. You can automatically scale up or down based on the workload. Autonomous Database is also very productive. It allows for focus on the data for solving business problems. ADB has self-service tools for analytics, data access, and it simplifies these difficult data engineering architectures. 04:43 Lois: OK…so can you tell us about running modern apps and analytics? Kay: Running applications means thinking about all the operational concerns and solving how to support mission-critical applications. Traditionally, this is where Oracle excels with high availability, security, operational solutions that have been proven over the years. Now, having developer tools and the ability to scale and reduce risk simplifies the development process without having to use complex sharding and data protection. Mission-critical capabilities that are needed for the applications are already provided in the functionality of the Oracle Data Management architecture. Disaster recovery, replication, backups, and security are all part of the Oracle Autonomous Database. 05:42 Kay: Even complex business-critical applications are supported by the operational security and availability of Oracle ADB. Transparently, it provides automated solutions for minimizing risk, dealing with complexity, and availability for all applications. Oracle's big picture data management strategy is simply complete and completely simple with the converged database, data management tools, and the best platform. It is focused on providing a platform that allows for modern app development across all data types, workloads, and development styles. It is completely scalable, available, and secure, leveraging the database technologies developed over several years. And it's available consistently across the environment. It is the simplest to use because of the available tools and running completely mission critical applications. 06:50 Nikita: Ah, so that’s how we come to… Kay: Simply complete and completely simple. Easy to remember and easy to incorporate into your existing architectures. Lois: OK. So Kay, can you talk a little bit more about Autonomous Database? 07:04 Kay: Let's compare Autonomous Database to how you ran the database on premise. How you ran the database on the cloud using our earlier Cloud Services, Database Cloud Services, and Oracle Exadata Cloud Service. The key thing to understand is Autonomous Database, or ADB, is a fully managed service. We fully manage the infrastructure. We fully manage the database for you. In on premise, you manage everything-- the infrastructure, the database, everything. We also have a service in between that that we call a co-managed service. Here we manage the infrastructure, and you manage the database. That service is important for customers who are not yet up to 19c. Or they might be running a packaged application like E-Business Suite. But for the rest of you, ADB is really the place you want to go. 08:09 Nikita: And why is that? Kay: Because it's fully managed and, because it's fully managed, is a much, much lower cost way to go. So when you talk to your boss about why he wants to move to ADB, they often care about the bottom line. They want to know like, am I going to lower my costs? And with ADB, because we take care of a lot of the tedious chores that DBAs normally have to do and because we take care of best practices, configurations, we can do things at a really low cost. 08:49 Lois: Kay, what does it take for a customer to move to Oracle’s Autonomous Database? Kay: We've got a tool that helps you look at your current database on prem. This tool will analyze what features you're using and let you know, hey, you know you're doing something that's not supported for ADB, for example. Like if you're running some release before 19c, we don't support it. If you're doing stuff like putting database tables in the system or sys schema, we don't support it. You know, there are a few things that very few customers do that we don't support. And this tool will flag those for you. And then the next step, it's pretty simple. You just use our Data Pump import/export tool to move your data out of your database on prem into the object store on the Cloud. And then you simply import-- you know how to use Data Pump to import-- the data off the file and the object store into the database. Then you're done. Pretty simple process. 09:57 Nikita: Do we assist our customers with data migration from on-prem to Cloud? Kay: More recently have come out with a new service on our Cloud called the Database Migration Service. With Autonomous Database Migration Service, you can just point us at your source database on prem or even on some other cloud. Whatever it is, we will take care of everything from there and move that, go through all the steps and move your database to ADB on the Cloud. Even better, we now are working with our Applications customers to make it really easy for them to move their packaged applications to Autonomous Database. The Oracle development teams that built JD Edwards, PeopleSoft, Siebel have now all certified that those packaged applications can run with Autonomous Database no problem. Our EBS team is working on it. And that'll be coming soon, sometime next year. 11:02 Lois: So, if I am an Apps customer, is there a special service for me? Kay: We have a fully managed service available on our Cloud that lets you take your entire application stack on the middle tier and the database tier, move it to our Cloud. Move the database part to Autonomous Database. And they will also manage your middle tier for you. 11:32 Want to get the inside scoop on Oracle University? Head on over to the all-new Oracle University Learning Community. Attend exclusive events. Read up on the latest news. Get first-hand access to new products and stay up-to-date with upcoming certification opportunities. If you are already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. Join the community today! 12:11 Nikita: Welcome back! Kay, can you talk a bit about APEX? Kay: We have this great tool called APEX or Application Express. We have a version of Autonomous Database just for any APEX application. Well, APEX is a low-code tool. It is our low-code tool that lets you rapidly build data-driven applications where the data is in the Oracle Database, really easy and really rapidly. We estimate at least 10 times faster than doing traditional coding to build your applications. What we're seeing is much, much higher productivity than that. Sometimes 40, even 50 times faster coding. 13:01 Kay: Out of the box, it comes with really nice tools for building things-- your classical forms and reporting kinds of workloads. It gives you things like faceted search and capabilities to do things like see on an e-commerce website where you get to choose things like dimensions, like I want a product where the cost is in this range. And, you know, it might have some other attributes. And it can very quickly filter that data for you and return the best results. And it's a really nice tool for iterating. Now, if your user interface doesn't look quite right, it's very easy to tweak colors and backgrounds and themes. Another reason it's so productive is that the whole middle tier part of your application is fully automated for you. You don't have to do anything about connection management or state management. You don't have to worry about mapping data types from some other 3GL programming language to data types. All of that is done for you. The combination of ADB and APEX really rocks. 14:17 Lois: Do we have Extract, Transform, and Load capabilities in our ADB? Kay: We have ETL transformation tools. Again, they let you specify transformations in a drag-and-drop fashion on the screen. We have all sorts of other tools and, in the service, the full power of the converged analytic technologies, things like graph analytics, spatial analytics, machine learning. All of this is built into this new platform. Now, a big, new capability around machine learning is something that we call AutoML. That lets any data scientists give us a data set, tell us what the key feature is that they want to analyze, and what the predictions are. And we will come up with a machine learning model for them out of the box. Really that easy. Plus, we have the low-code tool APEX that I mentioned earlier. 15:17 Kay: So this environment is really powerful for doing more than traditional data warehouses. We can build data lakes. We are integrated with the object stores on Oracle Cloud and also on other clouds. And we can do massively parallel querying of data in the core database itself and the data lake. 15:38 Nikita: Beyond the database tech, there’s the business side, right? How easy do we make a customer’s path to ADB from a business standpoint, a decision-making standpoint? Kay: So if you're an existing Oracle customer, you have an existing Oracle Database license you're using on prem, we have something called BYOL, Bring Your Own License, to OCI. We have the Cloud Lift Service. This huge cloud engineering team across all regions of the world will help you move your existing on-prem database to ADB for free. 16:16 Kay: And then, finally, we announced fairly recently something called the Support Rewards Program. This is something our customers are really excited about. It lets them translate their spending on OCI to a reduction in their support bill. So if you're a customer using OCI, you get a $0.25 to $0.33 reward for every dollar you spend on Oracle's Cloud. You can then take that money from your rewards and apply it to your bill for customer support, for your technology support even, like the database. And this is exactly what customers want as they move their investment to the cloud. They want to lower the costs of paying for their on-prem support. Now, we've talked about money. This lowers costs greatly. So ADB has lots of value. But the big thing I think to think about is really that it lowers costs. It lowers that cost via automation, higher productivity, less downtime, all sorts of areas. 17:22 Lois: You make a very convincing case for ADB, Kay. Kay: ADB is a great place to go. Take those existing Oracle Databases you have. Move and modernize them to a modern cloud infrastructure that's going to give you all the benefits of cloud, including agility and lower cost. So on our Cloud, we have something called the Always Free Autonomous Database Service. This service lets you get your hands on ADB. Try it out for yourself. You don't have to believe what we claim about how great this technology is. And we have other technologies like Live Labs that you can find on developer.oracle.com/livelabs that lets you do all kinds of exercises on this Always Free ADB infrastructure. Really get your hands dirty. And see for yourself how productive it can be. 18:16 Nikita: Thanks, Kay, for telling us about ADB and our database offerings. To learn more about this, head over mylearn.oracle.com, create a profile if you don’t already have one, and get started on our free Oracle Cloud Data Management Foundations Workshop. Lois: We hope you’ve enjoyed revisiting some of our most popular episodes these past few weeks. We’re kicking off the new year with a new season of the Oracle University Podcast. And this time around, it’ll be on Oracle Autonomous Database so make sure you don’t miss it. Until next week, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 18:52 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/28661233

Best of 2023: OCI Compute and Load Balancing 12/19/2023

Best of 2023: OCI Compute and Load Balancing In this episode, Lois Houston and Nikita Abraham, along with Rohit Rahi, look at two important services that Oracle Cloud Infrastructure provides: Compute and Load Balancing. They also discuss the basics of instances. Oracle MyLearn: Oracle University Learning Community: X (formerly Twitter): LinkedIn: Special thanks to Arijit Ghosh, Kiran BR, David Wright, the OU Podcast Team, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there. You’re listening to our Best of 2023 series, where over the last few weeks, we’ve been revisiting our most popular episodes of the year. 00:47 Nikita: In today’s episode, which is #5 of 6, we’ll listen in to a conversation Lois and I had earlier this year with Rohit Rahi, Vice President of CSS OU Cloud Delivery, on OCI Compute and Load Balancing. We began by asking Rohit why one would use Load Balancer. Lois: So let’s get right to it! 01:06 Rohit: You would use Load Balancer to achieve high availability and also achieve scalability. So typically the way Load Balancer works is, they're also referred to as Reverse Proxies, you would have a Load Balancer, which would be used accessed by multiple clients, various clients. And these clients would hit the Load Balancer, and the Load Balancer would proxy that traffic to the various backend servers. So in this way, it not only protects the various backend servers, but also provides high availability. In case a particular backend server is not available, the application can still be up and running. And then it also provides scalability because if lots of clients start hitting the Load Balancer, you could easily add more backend servers. And there are several other advanced capabilities like SSL termination and SSL passthrough and a lot of other advanced features. So the first type of Load Balancer we have in OCI is a layer 7 Load Balancer. Layer 7 basically means it understands HTTP and HTTPS. That's the OSI model. And then there are various capabilities available here. 02:13 Nikita: The Load Balancer comes in two different shapes, right? Can you tell us a little about that? Rohit: One is called a flexible shape where you define the minimum and the maximum and you define the range. And your Load Balancer can achieve any kind of-- support any kind of traffic in that particular range, going from 10 Mbps all the way to 8 Gbps. The second kind of shape is called dynamic where you predefine the shapes. So you have micro, small, medium, large, and you predefine the shape. And you don't have to warm up your Load Balancer. If the traffic comes to that particular shape, the Load Balancer automatically scales. 02:53 Rohit: You can always do a public and a private Load Balancer. Public means Load Balancer is available on the web. Private means your multiple tiers, like a web tier, can talk to your database tier and balance the traffic between them, but both tiers don't have to be public. A Load Balancer is highly available, highly scalable by design. 03:12 Lois: And what about the second type of Load Balancer? Rohit: The second kind of Load Balancer we have in OCI is called the Network Load Balancer. And as the name specify, Network Load Balancer operates at layer 4, layer 3, and layer 4 so it understands TCP, UDP, also supports ICMP. Again, like HTTP Load Balancer, it has both public and a private option, so you could create a public Network Load Balancer or a private Network Load Balancer. It's highly available, highly scalable, all those features are supported. 03:42 Nikita: Now, why would you use Network Load Balancer over an HTTP Load Balancer? Rohit: The primary reason you would use it is it's much faster than HTTP Load Balancer. It has much lower latency. So if performance is a key criteria for you, go with Network Load Balancer. On the contrary, the HTTP Load Balancer has higher level intelligence because it can look at the packets, it can inspect the packets, and it gets that intelligence. So if you're looking for that kind of routing intelligence, then go with HTTP Load Balancer. 04:15 Rohit: So OCI Compute service provides you virtual machines and bare metal servers to meet your compute and application requirements. The three defining characteristics of this service include this scalability, high performance, and lower pricing. So the first thing in the OCI Compute service is you have this notion of flexible shape. What does it mean? Well, it means you could choose your own course, your CPU processors, and you could also choose your own memory. Literally, there are thousands and thousands of configurations you can choose from. 04:49 Lois: But what’s the use of doing this? Rohit: The use of doing this is you could select the right machine type by using our flexible shapes. And in the cloud, there's this notion of T-shirt sizing. So you have a small, medium, large kind of shapes, and your application has to fit those shapes. And sometimes you overprovision or underprovision, and you have to go through that painful process of changing your machine types. We hope with this flexible shapes, you don't have to do that. 05:20 Rohit: If you still want to use the traditional approach, we have virtual machines, we have bare metal servers, and we have dedicated host. And you could use either one of them or all of them. And bare metal servers basically means you get a full machine, a full server which is completely dedicated to you. Dedicated host basically means that you get a full dedicated bare metal machine. But on top of that, you could run virtual machines. Not only this, but OCI is only one of the two cloud providers to provide you options on processors. So you can run AMD-based instances, you could run Intel-based instances, and you could also run Arm-based instances-- are really a powerful thing for mobile computing. The phones you are using today are probably running on Arm processors. Now, Arm is coming into the data centers. 06:16 Have something interesting to share with the Oracle University Learning Community? Present your topic at an exclusive community event. Help yourself by helping others. Start building your reputation and personal brand today. If you are already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 06:48 Nikita: What can you tell us about the pricing of this, Rohit? Rohit: On the pricing side, the service implements pay-as-you-go pricing. We are 50% cheaper than any other cloud out there, just to begin with. And not only that, you could use something like a Preemptable VMs to reduce your cost by more than 50% from your regular instances. Preemptable VMs are low cost, short lived VMs suited for batch jobs and fault tolerant workloads. These are similar to regular instances, but priced 50% lower. So you can use them to reduce your cost further. So when we say an instance, what we mean is a compute host. And it has several dependencies. So let's look at them. 07:31 Rohit: So you have an Oracle Cloud region here. A region is comprised of multiple ADs. An AD is nothing but a data center. The first dependency the compute service has or compute hosts have is on Virtual Cloud Network. So in order to spin up a compute instance, you need a Virtual Cloud Network. You have a network divided into smaller portions called subnets. So you have a subnetwork here, and you need to create these before you can spin up a compute host. 08:00 Rohit: Now you can spin up a compute host. It's a physical construct. Networking is a virtual construct. So how are they related? Within a compute host, you have a physical network interface card, and you virtualize that card. We give you this virtual NIC. And that virtual NIC is placed inside the subnet. And that's the association for the compute host. And that's where the private IP for the compute host comes from, because every compute host or VM you are running, or a bare metal machine, has a private IP address. Now, there is another set of dependency the compute instances have, and that's to the boot volume and the boot disk and the block volumes. 08:42 Lois: What does that mean, exactly? Rohit: Well, each of these compute hosts you are spinning up has an operating system. And the image that's used to launch an instance determines its operating system and other software. So you have this concept of an image that comes from this network storage disk called a boot disk. So it doesn't stay on the compute host, it's actually living on the network somewhere. And you also have data, like file systems, etc. You're working on the compute instances. They also live on the network. So there is the data disks and operating system disks together. There's a service called block volume service which the compute host uses to run its operating system and run its data disks. Now, these are remote storage. 09:33 Rohit: There is one more feature which is really relevant when you are talking about compute instances, and that's live migration. We know that computers fail all the time. So how do we make sure that whatever compute host you are running is always up and running, itself? So we have this feature called live migrate. And the idea here is if one of the compute hosts goes down, there's a problem, we would migrate your VM to another host in our data center, and it will be transparent to you. There are multiple options you provide-- whether opt-in or opt-out-- you can choose from. But the idea is we migrate your virtual machines so you can live-migrate between hosts without rebooting. This keeps your applications running even during maintenance events. To achieve this in your own data centers is a not-so-trivial task, but we make that seamless within OCI. 10:22 Nikita: Thanks for that, Rohit. To learn more about OCI, please visit mylearn.oracle.com, create a profile if you don’t already have one, and get started on our free OCI Foundations training. Lois: You will find skill checks that you can take throughout the course to ensure that you are on the right track. Nikita: We hope you enjoyed that conversation. Join us next week for our final throwback episode. Until then, this is Nikita Abraham... Lois: And Lois Houston, signing off! 10:54 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/28661168

Best of 2023: Networking in OCI 12/12/2023

Best of 2023: Networking in OCI When you work with Oracle Cloud Infrastructure, one of the first steps is to set up a virtual cloud network (VCN) for your cloud resources. In this episode, Lois Houston and Nikita Abraham, along with Rohit Rahi, discuss Oracle’s Virtual Cloud Network, VCN routing, and security. Oracle MyLearn: Oracle University Learning Community: X (formerly Twitter): LinkedIn: Special thanks to Arijit Ghosh, Kiran BR, Rashmi Panda, David Wright, the OU Podcast Team, and the OU Studio Team for helping us create this episode. --------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone. We hope you’ve been enjoying these last few weeks as we’ve been revisiting our most popular episodes of the year. 00:47 Lois: Today’s episode is the fourth of six we’ll have in this series and it’s a throwback to a conversation with Rohit Rahi, our Vice President of CSS OU Cloud Delivery, talking about Networking in OCI. We began by asking Rohit to explain what a Virtual Cloud Network is. Let’s listen in. 01:06 Rohit: At its core, it's a private software defined network you create in Oracle Cloud. It's used for secure communication. Whether instances talking to each other, instances talking to on-premises environments, or instances talking to other instances in different regions, you would use Virtual Cloud Network. It lives in an OCI region. Like we said, it's a regional service. It's highly available, massively scalable, and secure. And we take care of these things for you. So before we dive deep into the VCN and all the characteristics and all the features it has, let's look at some of the basic stuff. 01:44 Rohit: So the first thing is VCN has an address space. In this case, you see this address space is denoted in a CIDR notation. CIDR stands for classless interdomain routing. The VCN has an IP addressing range. And what that means is you have an address range. You take that range. And you can break it down into smaller networks which are called subnetworks. And these subnetworks are where you would instantiate your compute instances. 02:16 Nikita: And what can you tell us about the different mechanisms that exist inside a VCN? Rohit: So first, there is a notion of internet gateway. This is a gateway which is massively scalable, highly available, and is used for communication to anything on the internet. So if you have a web server which wants to talk to other websites on the web being able to be accessed publicly, you would use an internet gateway. So going to the internet and coming back from the internet. You also have this highly available, massively scalable router called NAT gateway. And it is used for providing NAT as a service. 02:53 Rohit: So what this means is the traffic is unidirectional. It can go from your private subnets to the internet. But users from the internet cannot use the NAT gateway to reach your instances running in a private subnet. So the idea with the NAT gateway is to enable outbound communication to the internet, but block inbound communications or connections initiated from the internet. Then we have another router which is called Service Gateway. And the idea is it lets resources in VCN access public OCI services such as object storage, but without using an internet or NAT gateway. So these are the three scenarios-- Internet gateway for internet, NAT gateway also for internet but unidirectional, and Service gateway for accessing OCI public services, which are available on the internet but accessing them in a secure manner. And then the other construct is called Dynamic Routing Gateway. This is a virtual router that provides a path for private traffic between your VCN and destinations other than the internet. 04:00 Lois: So what can these destinations be? Rohit: Well, this can be your on-premises environment. VCN uses route tables to send traffic out of the VCN to the internet, to on-premises networks, or to peered VCN, and we look at each of these scenarios. Route tables consist of a set of route rules. Each rule specifies a destination CIDR block and a route target. Think about route target as the next hop for the traffic that matches that destination CIDR block. Now, one thing to keep in mind is traffic within the VCN subnet is automatically handled by the VCN local routing. 04:44 Lois: Want to get the inside scoop on Oracle University? Head on over to the all-new Oracle University Learning Community. Attend exclusive events. Read up on the latest news. Get first-hand access to new products and stay up-to-date with upcoming certification opportunities. If you are already an Oracle MyLearn user, go to MyLearn to join the Community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. Join the Community today! 05:20 Nikita: Getting back to our discussion… if you have multiple networks, how do they talk to each other? Rohit: So there are two scenarios which are possible here. If the networks are within the same OCI region, they can talk to each other through a mechanism called local peering. If the two networks are in two different OCI data center regions, then you have the same concept, a similar concept, but it's a remote peering now. And instead of using local peering, now you're using the Dynamic Routing Gateways. Remember we talked about Dynamic Routing Gateways used for on-premises communication, anything which is not for internet. So this is also a use case for Dynamic Routing Gateway enabling communication between networks in different regions. 06:05 Rohit: So within VCN, you have this concept of security list. Think about security list as firewall rules associated with a subnet and applied to all instances inside the subnet. So what does it look like? The security list consists of rules that specify the type of traffic allowed in or out of the subnet. This applies to a given instance, whether it is talking with another instance in the VCN or a host outside the VCN. There's also another concept, which is called network security groups, or NSG. These are very similar construct as security list, but the key difference is these apply only to a set of virtual network interface cards in a single VCN. And another big difference here is NSGs can be the source or destination in the rules. Contrast this with the security list rules where you specify a CIDR, only a CIDR, as the source or destination. 07:06 Lois: Thanks for that, Rohit. To learn more about OCI, please visit mylearn.oracle.com, create a profile if you don’t already have one, and get started learning on our free OCI Foundations training. Nikita: You can also practice what you learn in a safe environment with our hands-on labs, without the anxiety of working in a live environment. 07:27 Nikita: We hope you enjoyed that conversation. Join us next week for another throwback episode. Until then, this is Nikita Abraham... Lois: And Lois Houston, signing off! 07:37 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/28661123

Best of 2023: OCI Identity and Access Management 12/05/2023

Best of 2023: OCI Identity and Access Management Data breaches occur more often than we’d like them to. As businesses embrace remote work practices, IT resources are more at risk than ever before. Oracle Identity and Access Management (IAM) is an essential tool for protecting enterprise resources against cybersecurity threats. Join Lois Houston and Nikita Abraham, along with Rohit Rahi, as they examine IAM and the key aspects of this service, and discuss how you can control who has access to your resources. Oracle MyLearn: Oracle University Learning Community: X (formerly Twitter): LinkedIn: Special thanks to Arijit Ghosh, Kiran BR, Rashmi Panda, David Wright, the OU Podcast Team, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi everyone. Thanks for joining us for this Best of 2023 series, where we’re playing you six of our most popular episodes of the year. 00:47 Nikita: Today’s episode is #3 of 6 and is a throwback to a conversation with Rohit Rahi, Vice President of CSS OU Cloud Delivery, on Identity and Access Management, which is one of OCI’s top security features. So, let’s get straight into it. 01:03 Rohit: IAM stands for Identity and Access Management service. It's also sometimes referred to as fine-grained access control or role-based access control service. There are two key aspects to this service. The first one is called authentication, or also referred to as AuthN. And the second aspect is referred to as authorization or also referred to as AuthZ. Authentication has to deal with identity or who someone is, while authorization has to deal with permission or what someone is allowed to do. 01:37 Rohit: So basically what the service ensures is making sure that a person is who they claim to be. And as far as authorization is concerned, what the service does is it allows a user to be assigned one or more pre-determined roles, and each roles comes with a set of permissions. Now, there are various concepts which are part of this service or various features which are part of this service, starting with identity domains, principles, groups, dynamic groups, compartments, et cetera. Now identity domains is basically a container for your users and groups. So think about this as a construct which represents a user population in OCI and the associated configurations and security settings. 02:30 Lois: So, how does this work in practice? Rohit: Well, what we do first is we create an identity domain, and then we create users and groups within that identity domain. And then we write policies against those groups, and policies are scoped to a tenancy, an account, or a compartment. And of course, the resources are available within a compartment. And again, compartment is kind of a logical isolation for resources. So this is how the whole service works. 03:03 Rohit: And users and the groups, authentication is done by common mechanisms like username and password, and policies is basically where you provide this role-based access control. So you put these groups in one of the pre-determined roles, and then you assign some permissions against those roles. So this is how the service works in a nutshell. Now anything you create in the cloud, all these objects, whether it's a block storage, it's a compute instance, it's a file storage, it's a database, these are all resources. And if these things are resources, there has to be a unique identifier for these resources, else how are you going to operate on these resources? So what OCI does is it provides its own assigned identifier, which is called Oracle Cloud ID, OCID. You don't have to provide this. We do this automatically for all the resources. 04:02 Nikita: Thanks for that rundown, Rohit. Another feature of OCI is compartments, right? Can you tell us a bit about compartments? Rohit: When you open an account in OCI, you get a tenancy. That's another fancy name for an account. And we also give you a Root Compartment. So think of Root Compartment as this logical construct where you can keep all of your cloud resources. And then what you could do is, you could create your own individual compartments. And the idea is, you create these for isolation and controlling access. And you could keep a collection of related resources in specific compartments. So the network resource has-- a network compartment has network resources, and storage compartment has storage resources. 04:46 Rohit: Now, keep in mind, Root Compartment, as I said earlier, can hold all of the cloud resources. So it can be sort of a kitchen sink. You could put everything in there. But the best practice is to create dedicated compartments to isolate resources. You will see why. Let me just explain. So first thing is, each resource you create belongs to a single compartment. So you create a virtual machine, for example. It goes to Compartment A. It cannot go to Compartment B again. You have to move it from Compartment A, or delete, and re-create in Compartment B. Keep in mind, each resource belongs to a single compartment. 05:21 Rohit: Why you use compartments in the first place is for controlling access and isolation. So the way you do that is, you have the resources, let's say in this case a block storage, kept in Compartment A. You don't want those to be used by everyone. You want those to be used only by the compute admins and storage admins. So you create those admins as users and groups, write these policies, and they can access these resources in this compartment. So it's very important. Do not put all of your resources in the Root Compartment. Create resource-specific compartments, or whichever way you want to divide your tenancies, and put resources accordingly. 06:00 Lois: Now, how do resources interact if they are in different compartments? Do they all have to be in the same compartment? Rohit: Absolutely not! Resources in one compartment can interact with the resource in another compartment. Here, the Virtual Cloud Network is-- the compute instance uses the Virtual Cloud Network, but these are in two different compartments. So this is absolutely supported. And it keeps your design much cleaner. Keep in mind that resources can also be moved from one compartment to another. So in this example, Compartment A had a virtual machine. We can move that from Compartment A to Compartment B. Another concept, which is very important to grasp is the compartments are global constructs, like everything in identity. So resources from multiple regions can be in the same compartment. So when you go to Phoenix, you see this compartment existing. You go to Ashburn, you see the same compartment. 06:55 Rohit: Now, you can write policies to prevent users from accessing resources in a specific region. You could do that. But keep in mind, all the compartments you create are global, and they are available in every region you have access to. Compartments can also be nested. So you have up to six levels nesting provided by compartments. You would do this again because this can mimic your current design, whether it's your organizational design or whether it's your ID hierarchy. You could create nested compartments. It just helps keep your design cleaner. 07:32 Rohit: And then, finally, you could set quotas and budgets on compartments. So you could say that, my particular compartment, you cannot create a bare metal machine. Or you cannot create an Exadata resource. So you could control it like that. And then you could also create budgets on compartments. So you could say that, if the usage in a particular compartment goes beyond $1,000, you'd get flagged, and you get notified. So you could do that. So that's compartments for you. It's a very unique feature within OCI. We believe it helps keep your tenancies much better organized. And it really supports your current ID hierarchy and design. 08:12 Boosting your professional reputation is now easier than ever. Oracle University Learning Community is a collaborative, dynamic community that gives you the power to create your own personal brand. Achieve champion levels and acquire badges. Get inducted into the Hall of Fame. Become a thought leader. If you are already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 08:53 Nikita: Welcome back! So Rohit, can you tell us a little bit about principals? Rohit: A principal is an IAM entity that is allowed to interact with OCI resources. There are two kinds of principals primarily in OCI. One is your users. Think about people who are logging on to your console or using your CLI or SDKs, users… human beings actually using your cloud resources. And then the resources themselves can be principals. So a good example of a resource principal is an instance principal which is actually an instance which becomes a principal, which means that it can make API calls against other OCI services like storage. 09:34 Rohit: Also, when we talk about principles we have groups. And groups are basically collection of users who have the same type of access requirements to resources. So you can have a storage admin group where you could group all the human beings who are storage administrators and so on and so forth. So let's look at some of the details, starting with authentication. Authentication is sometimes also referred to as AuthN. Authentication is basically figuring out are you who you say you are. And the easiest way to understand this is all of us deal with this on everyday basis. When you go to our website and you provide your username and password to access some of the content, you are being authenticated. 10:15 Rohit: There are other ways to do authentication. The one common for cloud is API Signing Keys. So when you are making API calls, whether you're using the SDK or the CLI, you would use the API Signing Keys which use a public private key pair to sign these APIs calls and authenticate these APIs calls. It uses an RSA key pair, with both a public key and a private key. There is also a third way to do authentication, and that's based on authentication tokens. And these are Oracle-generated token strings. And the idea here is you can authenticate third-party APIs which don't support OCI authentication model. 10:56 Lois: So, then, what are authorizations? Rohit: So authorization deals with permissions and figuring out what permissions do you have. In OCI, authorization is done through what we call as IAM policies. And policies, think about these as human readable statements to define granular permissions. Remember, policies can be attached to a compartment or they could be attached to a tenancy. If they're attached to a tenancy, it applies to everything within that tenancy. If it's applied to a compartment, it applies to only the resources within that compartment. 11:33 Rohit: The syntax is always you have to start with an allow. Everything is denied by default, so you don't really have to write a deny statement. So you say allow group_name. A group is basically a collection of users. So you cannot write a policy on individual users, you always operate at a group level. To do something, there's a verb. On some resources, there's a resource-type and there's a location. Location can be a tenancy. Location can be a compartment. And you can make these policies really complex with adding conditions. So just to give you an idea of what the verbs might look like. There are four levels of verb. There is a manage, there's a use, there's a read, and there's a inspect. And as you go down, these become additive. 12:17 Rohit: So manage basically means you can manage your resources, use basically means you can read but you could not do things like update and delete and so on and so forth. And you can read more on the documentation. Resource type basically can be all resources, meaning everything in your account, or it could be compute resources, database resources, whatnot, all the resources you have. Now, you could operate at a family level, which is meaning all the entities within that resource family, or you could even go very granular. So you could say that in compute, I just want somebody to operate on the instances, but not work on the instance images. So you could actually do that. So this is how you would write a policy. 12:58 Nikita: For more on OCI, please visit mylearn.oracle.com, create a profile if you don’t already have one, and get started on our free training on OCI Foundations. Taking this training will help you advance and future-proof your career and prepare you for our OCI Foundations Associate exam. Nikita: We hope you enjoyed that conversation. Join us next week for another throwback episode. Until then, this is Nikita Abraham... Lois: And Lois Houston, signing off! 13:27 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/28656723

Best of 2023: Getting Started with Oracle Cloud Infrastructure 11/28/2023

Best of 2023: Getting Started with Oracle Cloud Infrastructure Oracle’s next-gen cloud platform, Oracle Cloud Infrastructure, has been helping thousands of companies and millions of users run their entire application portfolio in the cloud. Today, the demand for OCI expertise is growing rapidly. Join Lois Houston and Nikita Abraham, along with Rohit Rahi, as they peel back the layers of OCI to discover why it is one of the world's fastest-growing cloud platforms. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, Kiran BR, Rashmi Panda, David Wright, the OU Podcast Team, and the OU Studio Team for helping us create this episode. ------------------------------------------------------ Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Principal Technical Editor. Nikita: Hi there! You’re listening to our Best of 2023 series, where over the next few weeks, we’ll be revisiting six of our most popular episodes of the year. 00:47 Lois: Today is episode 2 of 6, and we’re throwing it back to our very first episode of the Oracle University Podcast. It was a conversation that Niki and I had with Rohit Rahi, Vice President, CSS OU Cloud Delivery. During this episode, we discussed Oracle Cloud Infrastructure’s core coverage on different tiers. Nikita: But we began by asking Rohit to explain what OCI is and tell us about its key components. So, let’s jump right in. 01:14 Rohit: Some of the world's largest enterprises are running their mission-critical workloads on Oracle's next generation cloud platform called Oracle Cloud Infrastructure. To keep things simple, let us break them down into seven major categories: Core Infrastructure, Database Services, Data and AI, Analytics, Governance and Administration, Developer Services, and Application Services. But first, the foundation of any cloud platform is the global footprint of regions. We have many generally available regions in the world, along with multi-cloud support with Microsoft Azure and a differentiated hybrid offering called Dedicated Region Cloud@Customer. 01:57 Rohit: We have building blocks on top of this global footprint, the seven categories we just mentioned. At the very bottom, we have the core primitives: compute, storage, and networking. Compute services cover virtual machines, bare metal servers, containers, a managed Kubernetes service, and a managed VMWare service. These services are primarily for performing calculations, executing logic, and running applications. Cloud storage includes disks attached to virtual machines, file storage, object storage, archive storage, and data migration services. 02:35 Lois: That’s quite a wide range of storage services. So Rohit, we all know that networking plays an important role in connecting different services. These days, data is growing in size and complexity, and there is a huge demand for a scalable and secure approach to store data. In this context, can you tell us more about the services available in OCI that are related to networking, database, governance, and administration? 03:01 Rohit: Networking features let you set up software defined private networks in Oracle Cloud. OCI provides the broadest and deepest set of networking services with the highest reliability, most security features, and highest performance. Then we have database services, we have multiple flavors of database services, both Oracle and open source. We are the only cloud that runs Autonomous Databases and multiple flavors of it, including OLTP, OLAP, and JSON. And then you can run databases and virtual machines, bare metal servers, or even Exadata in the cloud. You can also run open source databases, such as MySQL and NoSQL in the Oracle Cloud Infrastructure. 03:45 Rohit: Data and AI Services, we have a managed Apache Spark service called Dataflow, a managed service for tracking data artifacts across OCI called Data Catalog, and a managed service for data ingestion and ETL called Data Integration. We also have a managed data science platform for machine learning models and training. We also have a managed Apache Kafka service for event streaming use cases. Then we have Governance and Administration services. These services include security, identity, and observability and management. We have unique features like compartments that make it operationally easier to manage large and complex environments. Security is integrated into every aspect of OCI, whether it's automatic detection or remediation, what we typically refer as Cloud Security Posture Management, robust network protection or encryption by default. We have an integrated observability and management platform with features like logging, logging analytics, and Application Performance Management and much more. 04:55 Nikita: That’s so fascinating, Rohit. And is there a service that OCI provides to ease the software development process? Rohit: We have a managed low code service called APEX, several other developer services, and a managed Terraform service called Resource Manager. For analytics, we have a managed analytics service called Oracle Analytics Cloud that integrates with various third-party solutions. Under Application services, we have a managed serverless offering, call functions, and API gateway and an Events Service to help you create microservices and event driven architectures. 05:35 Rohit: We have a comprehensive connected SaaS suite across your entire business, finance, human resources, supply chain, manufacturing, advertising, sales, customer service, and marketing all running on OCI. That's a long list. And these seven categories and the services mentioned represent just a small fraction of more than 80 services currently available in OCI. Fortunately, it is quick and easy to try out a new service using our industry-leading Free Tier account. We are the first cloud to offer a server for just a penny per core hour. Whether you're starting with Oracle Cloud Infrastructure or migrating your entire data set into it, we can support you in your journey to the cloud. 06:28 Have an idea and want a platform to share your technical expertise? Head over to the new Oracle University Learning Community. Drive intellectual, free-flowing conversations with your peers. Listen to experts and learn new skills. If you are already an Oracle MyLearn user, go to MyLearn to join the Community. You will need to log in first. If you have not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. Join the conversation today! 07:04 Nikita: Welcome back! Now let’s listen to Rohit explain the core constructs of OCI’s physical architecture, starting with regions. Rohit: Region is a localized geographic area comprising of one or more availability domains. Availability domains are one or more fault tolerant data centers located within a region, but connected to each other by a low latency, high bandwidth network. Fault domains is a grouping of hardware and infrastructure within an availability domain to provide anti-affinity. So think about these as logical data centers. Today OCI has a massive geographic footprint around the world with multiple regions across the world. And we also have a multi-cloud partnership with Microsoft Azure. And we have a differentiated hybrid cloud offering called Dedicated Region Cloud@Customer. 08:02 Lois: But before we dive into the physical architecture, can you tell us…how does one actually choose a region? Rohit: Choosing a region, you choose a region closest to your users for lowest latency and highest performance. So that's a key criteria. The second key criteria is data residency and compliance requirements. Many countries have strict data residency requirements, and you have to comply to them. And so you choose a region based on these compliance requirements. 08:31 Rohit: The third key criteria is service availability. New cloud services are made available based on regional demand at times, regulatory compliance reasons, and resource availability, and several other factors. Keep these three criteria in mind when choosing a region. So let's look at each of these in a little bit more detail. Availability domain. Availability domains are isolated from each other, fault tolerant, and very unlikely to fail simultaneously. Because availability domains do not share physical infrastructure, such as power or cooling or the internal network, a failure that impacts one availability domain is unlikely to impact the availability of others. A particular region has three availability domains. One availability domain has some kind of an outage, is not available. But the other two availability domains are still up and running. 09:26 Rohit: We talked about fault domains a little bit earlier. What are fault domains? Think about each availability domain has three fault domains. So think about fault domains as logical data centers within availability domain. We have three availability domains, and each of them has three fault domains. So the idea is you put the resources in different fault domains, and they don't share a single point of hardware failure, like physical servers, physical rack, top of rack switches, a power distribution unit. You can get high availability by leveraging fault domains. We also leverage fault domains for our own services. So in any region, resources in at most one fault domain are being actively changed at any point in time. This means that availability problems caused by change procedures are isolated at the fault domain level. And moreover, you can control the placement of your compute or database instances to fault domain at instance launch time. So you can specify which fault domain you want to use. 10:29 Nikita: So then, what’s the general guidance for OCI users? Rohit: The general guidance is we have these constructs, like fault domains and availability domains to help you avoid single points of failure. We do that on our own. So we make sure that the servers, the top of rack switch, all are redundant. So you don't have hardware failures or we try to minimize those hardware failures as much as possible. You need to do the same when you are designing your own architecture. So let's look at an example. You have a region. You have an availability domain. And as we said, one AD has three fault domains, so you see those fault domains here. 11:08 Rohit: So first thing you do is when you create an application you create this software-defined virtual network. And then let's say it's a very simple application. You have an application tier. You have a database tier. So first thing you could do is you could run multiple copies of your application. So you have an application tier which is replicated across fault domains. And then you have a database, which is also replicated across fault domains. 11:34 Lois: What’s the benefit of this replication, Rohit? Rohit: Well, it gives you that extra layer of redundancy. So something happens to a fault domain, your application is still up and running. Now, to take it to the next step, you could replicate the same design in another availability domain. So you could have two copies of your application running. And you can have two copies of your database running. 11:57 Now, one thing which will come up is how do you make sure your data is synchronized between these copies? And so you could use various technologies like Oracle Data Guard to make sure that your primary and standby-- the data is kept in sync here. And so that-- you can design your application-- your architectures like these to avoid single points of failure. Even for regions where we have a single availability domain, you could still leverage fault domain construct to achieve high availability and avoid single points of failure. 12:31 Nikita: Thank you, Rohit, for taking us through OCI at a high level. Lois: For a more detailed explanation of OCI, please visit mylearn.oracle.com, create a profile if you don’t already have one, and get started on our free training on OCI Foundations. Nikita: We hope you enjoyed that conversation. Join us next week for another throwback episode. Until then, this is Nikita Abraham... Lois: And Lois Houston, signing off! 12:57 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/28656633

Best of 2023: Multicloud is the Way to Go 11/21/2023

Best of 2023: Multicloud is the Way to Go Sergio Castro joins Lois Houston and Nikita Abraham to explore multicloud, some of its use cases, and the reasons why many businesses are embracing this strategy. A-Team Chronicles: Oracle University Blog: Oracle MyLearn: Oracle University Learning Community: X (formerly Twitter): LinkedIn: Special thanks to Arijit Ghosh, David Wright, the OU Podcast Team, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! If you’ve been following along with us, you’ll know we just completed our first three seasons of the Oracle University Podcast. We’ve had such a great time exploring OCI, Data Management, and Cloud Applications business processes. And we’ve had some pretty awesome special guests, too. 00:56 Nikita: Yeah, it’s been so great having them on and so educational so do check out those episodes if you missed any of them. Lois: As we close out the year, we thought this would be a good time to revisit some of our most popular episodes with you. Over the next few weeks, you’ll be able to listen to six of our most popular episodes from this year. Nikita: Right, this is the best of the best–according to you–our listeners. 01:20 Lois: Today’s episode is #1 of 6 and is a throwback to a discussion with our Principal OCI Instructor Sergio Castro on multi-cloud. Keep in mind that this chat took place before the release of Oracle University’s course and certification on multi-cloud. It’s available now on mylearn.oracle.com so if it interests you, you should go check it out. Nikita: We began by asking Sergio to help us with the basics and explain what multi-cloud is. So, let’s dive right in. Here we go! 01:51 Sergio: Good question. So multi-cloud is leveraging the best offering of two or more cloud service providers. This as a strategy for an IT solution. And Oracle embraces multi-cloud. This strategy was clearly communicated during Open World in Las Vegas last year. We even had demos where OCI presenters opened the cloud Graphic User Interface of other providers during our live sessions. So the concise answer to the question is multi-cloud is two or more cloud vendors providing a consolidated solution to a customer. 02:29 Nikita: So, would an example of this be when a customer uses OCI and Azure? Sergio: Absolutely. Yes, exactly. That's what it is. We can say that our official multi-cloud approach started with the interconnect agreement with Azure. But customers, they have already been leveraging our FastConnect partners for interconnecting with other cloud providers. The interconnect agreement with Azure just made it easier. Oracle tools such as Oracle Integration and Golden Gate have been multi-cloud ready even prior to our official announcement. And if you look at the Oracle's document... the documents from Oracle, you can find VPN access to other cloud providers, but we can talk about that shortly. 03:16 Nikita: OK. So, why would organizations use a multi-cloud strategy? What do they gain by doing that? Sergio: Oh, there are many reasons why organizations might want to use a multi-cloud strategy. For example, a customer might want to have vendor redundancy. Having the application running with one vendor and having the other vendor just stand by in case something goes wrong with that cloud provider. So it is best practices not to rely on just one cloud service provider. Another customer might want to have the application with one tier or the application tier with one cloud provider and their database tier with another cloud provider. 03:53 Sergio: So this is a solution leveraging the best to cloud providers. Another company or another reason might be a company acquired another one, you know purchasing a second company, and they have different cloud providers and they just want to integrate their cloud resources. So every single cloud provider offer unique solutions and customers want to leverage these strong points. For example, we all know that AWS was the first infrastructure access service provider, and the industry adopted them. Then other players came along like OCI and customers realized that there are better and less expensive options that now they can take advantage of. So cloud migration is another reason why multi-cloud interconnectivity is needed. 04:42 Lois: Wow! There really are a lot of different use cases for multi-cloud. Sergio: Yeah, absolutely. There is, Lois. So Golden Gate, for example, this is an Oracle product. Oracle Golden Gate allows replication from two different databases. So if a customer wants to replicate the Oracle Database in OCI, in Oracle Cloud Infrastructure, to a SQL server in Azure, this is possible. And now there's an OCI to Azure interconnect (live) and it can facilitate this, this database replication. And if a start-up needs to communicate OCI to Google Cloud Platform, for example, but a digital circuit is not economically viable, then we have published step-by-step configuration instructions for site-to-site VPN, and this includes all the steps on the Google Cloud Platform as well. So these are some of the different use cases. 05:37 Lois: So, what should you keep in mind when you're designing a multi-cloud solution? Sergio: The first thing that comes to mind is business continuity. It is very important to have High Availability and Disaster Recovery strategies. This to keep the lights on and focus on the organization's current technology, the organization's current needs, the company's vision, and the offering from the cloud service providers out there. The current offerings that each cloud service provider brings to this company. For example, if an organization's on-premises, current deployment consists of Microsoft applications and Oracle Databases, and they want to use as much as they can of their current knowledge base that their staff has acquired through the years, it only makes sense to take the apps to Azure and the database to Oracle Cloud Infrastructure and either leverage ODSA, Oracle Database Solution for Azure, or our OCI-Azure interconnect regions. We have 12 of those. 06:39 Sergio: So ODSA was designed with Azure cloud architects in mind. The Oracle Database solution for Azure. For each database provision using ODSA, the service delivers OCI database metrics, OCI events, and OCI logs to tools such as Azure Application Insights, Azure Event Grid, and Azure Log Analytics. But the concise key points to keep in mind are latency, security, data movement, orchestration, and operation management. 07:10 Nikita: So, latency... security... Can you tell us a little bit more about these? Sergio: Yes, latency is crucial. If an application needs, let's say X milliseconds, 3 milliseconds response time, the multi-cloud solution better meet these needs. We recently published a blog post where we released the millisecond response of our 12 interconnect sites to Azure and OCI. We have 12 interconnect sites of Azure regions to 12 regions from OCI. Now, regarding security, in Oracle, we pride ourselves for being a security company. Security is at our core of who we are and we have taken this approach to multi-cloud. This for encryption of data at rest, encryption of data in transit, masking the data in the database, security key management, patching service, Identity and Access Management, Web Application Firewall. All of these solutions from Oracle are very well suited for multi-cloud approach. 08:17 Lois: OK, what about data movement, orchestration and operation management? You mentioned those. Sergio: I mentioned Golden Gate earlier. So you can use this awesome tool for replication. You can also use this for migration. But data movement is much more than replication, like real live transactions taking place and backup strategies. We have options for all of this. Our object storage, our bulky regions backup strategies. Now for orchestration, the Oracle API Gateway avoids vendor lock-in and enables you to publish APIs with private endpoints that are accessible from within your network and which you can expose with a public IP address. This in case you want to accept traffic from the internet. 09:07 Nikita: Ah, that makes sense. Thanks for explaining those, Sergio. Now, what multi-cloud services does OCI have? Sergio: So I already mentioned a few like ODSA, the Oracle Database Solution for Azure. So, this is where Azure customers can easily provision, access, and operate an Oracle Database enterprise-grade and the Oracle Cloud Infrastructure with a familiar Azure-like experience. ODSA was jointly announced back in July 2022 by our CTO Larry Ellison and Microsoft’s Satya Nadella. He's the CEO. This was last year. And we also announced the MySQL Heatwave, which is available on AWS. This solution offers online transactional processing analytics, machine learning, and automation with a single, MySQL database. So OCI multi-cloud approach started when the OCI regions interconnected via FastConnect to Azure regions Express Route. This was back in June of 2019. 10:12 Sergio: Other products for multi-cloud include OCI integration services, OCI Golden Gate, the Oracle API Gateway, Observability and Management, and Oracle Data Sync to name a few. Nikita: So we've been working in multi-cloud services since 2019. Interesting. Lois: It really is. Sergio, can you tell us a little bit about the type of organizations that can benefit from multi-cloud? 10:36 Sergio: Absolutely. My pleasure. So organizations of all sizes and of all industries can benefit from multi-cloud, from start-ups to companies in the top 100 of the Forbes list and from every corner of the world, you name it, every corner of the world. So it's available worldwide for customers, the Oracle customers. There are also customers, and we know this of other providers. So in terms of cloud, it's to the customers' benefit that cloud service providers have a multi-cloud strategy. In OCI <inaudible>, OCI has been a pioneer in multi-cloud. It was in 2019 when the FastConnect to Express Route partnership was announced. And Site-to-Site VPN is also available to all three of our major cloud competitors. So the beauty of the last word, cloud competitors, is that indeed they are our competitors and we try to win businesses away from them. 11:29 Sergio: But at the same time, our customers demand the ability for cloud providers to work with each other and our customers are right. And for this reason, we embrace multi-cloud. Recently, the federal government announced that they selected four cloud providers: OCI, AWS, Azure, and Google Cloud Platform. And also, Uber announced a major deal with OCI and Google Cloud Platform. So these customers, they want us to work together. So multi-cloud is a way to go, strategy and we want to make our customers happy. So we will operate and work with these cloud providers, service providers. 12:09 Nikita: That's really great. So a customer can take advantage of the benefits of OCI, even if they have other services running on another cloud provider. Now if I wanted to become a multi-cloud developer or a cloud architect, how would I go about getting started? Is there a certification I can get? Sergio: Absolutely. Excellent question. I love this question. So this depends on where you are in your cloud journey. If you are already a cloud knowledgeable engineer with either AWS or Azure, you can start with our OCI for Azure Architect and OCI for AWS Architect. We have courses for both. And if you are just getting started with cloud and you want to learn OCI, you can start with our OCI Foundations as the path to OCI and as you progress along, we have OCI Architect Associate, we have OCI Architect Professional. So there's a clear path, but if you have a specialty like a developer's or operations or multi-cloud certification, so we have all of this for you. And regarding the OCI Architect Professional certification, it contains in the learning path a lesson and a demo on how to interconnect OCI and Azure from the ground up. 13:23 Lois: And all of this training is available for free on mylearn.oracle.com, right? Sergio: Yes, that is correct, Lois. Just visit the site, mylearn.oracle.com, and create an account. The site keeps track of your learning progress and you can always come back and continue from where you left off, at your own speed. 13:42 Lois: That's great. And what if I don't want to get certified right now? Sergio: Of course, you do not have to be pursuing a certification to gain access to the training in MyLearn. If you are only interested in the OCI to Azure interconnection lesson, for example, you can go right to that course in MyLearn, bypassing all the other material. Just watch that lesson. If you're interested, follow along with the demo on your own environments. 14:09 Nikita: So you can take as much or as little training as you want. That's wonderful. Sergio: Absolutely it is. And with regards to other OCI products that are great for multi-cloud, our API Gateway is greatly covered in our OCI Developer Professional certification. The awesome news that I'm bringing to you right now is that soon Oracle University will release a new OCI multi-cloud certification. This is going to be accompanied by with the learning path and the multi-cloud certification, this is what I'm currently at this moment working on. We are designing the material. We are having fun right now doing the labs, and shortly, we will write the test questions. 14:51 Lois: That's great news. You know I love to share a sneak peek at new training we're working on. Thank you so much, Sergio, for giving us your time today. This was really insightful. Sergio: On the contrary, thank you. And thanks to everyone who's listening. I encourage you to go ahead and link your multiple cloud accounts and if you have questions, feel free to reach out. You can find me in the Oracle University Learning Community. 15:15 Nikita: We hope you enjoyed that conversation. And like we were saying before, the multi-cloud course has been released and has quickly become one of our most sought-after certifications. So, if you want to access the multi-cloud course, visit mylearn.oracle.com. Lois: Join us next week for another throwback episode. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 15:39 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/28621813

The Accounting Transformation and Budget to Report Process Flows 11/14/2023

The Accounting Transformation and Budget to Report Process Flows In the final episode of this season, hosts Lois Houston and Nikita Abraham, along with Sr. Principal ERP Learning Strategist David Barnacle, dive into the Accounting Transformation process flow, which covers how financial transactions are converted into journal entries and how subledger journal entries are processed through subledger accounting. They also explore the Budget to Report process flow, which focuses on planning, accounting for transactions, and reporting financial information to the appropriate stakeholder. Budget reporting goes a long way in helping businesses take corrective actions and improve their financial performance. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Radhika Banka, Parvathy Narayan, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Last week, we had David “Barney” Barnacle, Sr. Principal ERP Learning Strategist, with us, who spoke about Procure to Pay and Asset Acquisition to Retirement, which are two major business processes within the Oracle Financials Business Process Model. Barney is here with us for one last time this season to take us through the last two business processes, Accounting Transformation and Budget to Report. 01:02 Nikita: Welcome back, Barney! Barney: Hi Niki! Hi Lois! Nikita: So Barney, what can you tell us about Accounting Transformation? Barney: Accounting Transformation is one of the most important business processes in the Oracle Cloud Financials Business Process Model. All our enterprises are required to record their financial transactions, and the Oracle Fusion Cloud: ERP application supports businesses in recording these transactions with the help of best practice life cycles like Invoice to Cash, Procure to Pay, and Asset Acquisition to Retirement. 01:32 Nikita: Everything we’ve discussed in our previous episodes. Barney: Right. Now, Accounting Transformation refers to the process of converting business transactions from Oracle subledgers, or transactions from external source systems, into detailed, auditable journal entries. Source systems are typically industry-specific applications that are either purchased from third parties or built internally within the customer organization. Examples of such systems include core banking applications, insurance policy administration applications, billing applications, and point of sales applications. And to do this transformation, we have a very powerful tool called the Accounting Engine. If the accounting engine is only used in Oracle Cloud Subledgers (for example, Assets, Payables, etc.), then this engine is referred to as the Subledger Accounting Engine. 02:24 Lois: And what does this Subledger Accounting Engine do? Barney: The Subledger Accounting Engine, also known as SLA, is loaded with predefined event models and accounting methods, i.e. the accounting rules. And within this engine, users can also create user-defined accounting methods, i.e. new rules, to achieve multiple financial reporting requirements. The accounting engine’s job is to convert business transactions into auditable and balanced accounting journal entries. 02:55 Lois: Is SLA a separate product? Barney: SLA is not a separate product itself but is Oracle’s common engine, which caters to the accounting needs of all the Oracle subledgers. Subledger Accounting is a rules-based accounting engine that is centralized for use by all the Oracle Cloud subledgers. 03:13 Nikita: So how does Subledger Accounting work? Barney: When using Oracle Cloud Financials, financial transactions such as invoices or payments are recorded in the Oracle Cloud subledger products, whereas transactions from legacy systems are recorded in Oracle Fusion Cloud Accounting Hub. Each financial transaction has some accounting event type associated with it. For example, creating a customer invoice, adjusting a payment, validating a supplier invoice, and so on. As I was saying earlier, Subledger Accounting has predefined accounting rule sets, also known as accounting methods. And these accounting methods follow industry practices (for example, Standard accruals). 03:53 Nikita: And how do accounting rules work? Barney: The accounting rules pick the accounting event type associated with the business transaction. It uses relevant transaction attributes like Amounts, Currencies, Dates, Customers, or Suppliers. Then, it converts the transactional attributes into balanced and auditable Subledger and ultimately General Ledger journal entries, which may also require the copying or complete creation of account code combinations. 04:19 Lois: Can all the accounting requirements of a business be met with the help of standard accounting methods? Barney: No, Lois. Sometimes, standard accrual accounting methods don’t meet all the accounting or business requirements. But then subledger accounting can support user-defined accounting methods to generate different accounting entries to support these different regulatory or business requirements. For example, by using a local GAAP. 04:44 Nikita: Barney, can you tell us in more detail the various steps involved in the accounting transformation process? Barney: So, the first step is to record business transactions using modern business life cycles. As the user processes these transactions, with such actions as create, validate, adjust, delete etc., these actions are recorded as event types. The accounting engine uses these types and the accounting method rules to create detailed subledger journals. It is the accounting rules that take the transaction source attributes, such as amount, date, customer supplier, etc., and converts them into a balanced detailed subledger journal that can be audited. If there are insufficient or incorrect account or no account values within the source transactions, then the account rules, mapping sets, and user-defined formula that can be configured to create the correct account combinations. 05:39 Barney: To create these journals, the create accounting process can be automated to run on a regular basis, typically at least once a day. The Create Accounting process first generates detailed subledger journals in draft or final mode within the SLA data repository. If these SLA journals are in final mode, then they can also be transferred to create summarized or detailed general ledger journals. Once posted, these GL journals update the account balances of all dimensions stored within the GL Essbase cubes. From these account balances, you can create flexible financial reports to meet the requirements of all stakeholders. And the best part is any role that’s assigned SLA privileges can carry out these tasks. 06:30 Have an idea for a new course or learning opportunity? We’d love to hear it! Visit the Oracle University Learning Community and share your thoughts with us. Your suggestion could find a place in future development projects. If you’re already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you’ve not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 06:59 Nikita: Welcome back. So, transactions are created, accounting is generated using the powerful SLA engine, and then when that’s done, organizations can publish their financial reports and submit them to government authorities and their stakeholders, right? So, how do they administer control over their financial planning and spending? And how do organizations create these different reports? 07:23 Barney: Financial reports/statements are key to assessing the financial efficiency and determining the key performance indicators of any organization or enterprise. In Oracle Fusion Cloud, we talk of producing reports across three key axes, the legal, the management, and the functional axis, to match the varying requirements of stakeholders. Some organizations, to drive good financial control, plan and generate budgets and/or forecasts. This is so that they can estimate their revenue and expenses for a specific future period. In fact, some enterprises go much further and use budgetary control and encumbrance accounting to ensure expenditure remains within budgeted control levels per period and they can block further expenditure on items that have spent over planned budgeted amounts. 08:15 Barney: Other enterprises may have a rolling 12-month budget that can be updated at the end of each financial period. Simple to complex budgets or forecasts can be loaded into the GL Essbase cubes and the planned budgeted account balances over a period can easily be compared with actual performance using a variety of financial reporting tools provided by Oracle Cloud. Any budget variance can be used to drive financial control and analysis, while contributing to effective, strategic decision-making. The Oracle Fusion Cloud Budget to Report process focuses on planning, accounting for transactions, and reporting financial information to the appropriate stakeholder. 08:59 Lois: Why is this process so important for organizations? What are the benefits of budget reporting? Barney: It is a great way to drive financial control by efficiently tracking the company's performance versus the budget or forecast plan. Budget reporting allows an organization to perform frequent comparisons of forecasted and actual results with the purpose of fixing the key deviations. It allows organizations to allocate cash to assets worth the investment, make acquisitions, or create disposals or disinvestment strategies. 09:32 Lois: Barney, what are the key processes within Budget to Report? Barney: Within the Budget to Report processes life cycle, there are three key subprocesses: managing budgets and forecasts, capturing transactions (i.e. account balances), and period close to financial reporting. Accountants will cycle through these three processes on a regular basis, which is typically monthly. Let’s start with the Manage Budgets and Forecasts process. This process refers to the entire cycle of events that start with planning and formulating and ultimately ends with creating budgets and forecasts in the application. Oracle General Ledger simplifies budget and forecast uploads into the system by the use of Excel spreadsheets. 10:15 Barney: Next is the Capture Transactions and Journal Entry process. Financial transactions captured in the subledgers are accounted for via the SLA accounting engine and are converted into detailed subledger and summarized general ledger journals (i.e. the accounting process we have just discussed under SLA). Manual journals can also be created with the use of the user interface or via spreadsheet uploads. The account combinations on these journal lines, once posted, that record the actual account balances, which detail organization revenue, expenditure, taxation, and so on over a period. 10:52 Barney: The Period Close to Financial Reporting process starts with the period closure for each subledger application, ensuring all financial transactions are captured and reported in the correct period. It includes the reconciliation of all key suspense accounts or key accounts (for example, cash balances, tax debtors, liabilities, etc.), special period-end processing, such as foreign currency requirements for revaluation and translations or allocation journals to spread the account distribution of central costs or revenue pools, and the use of consolidation ledgers, with requirements to move currency account balances between ledgers. Finally, from these consolidated, reconciled account balances, a variety of reporting tools can be used to generate the required financial reports/statements for both internal and external stakeholders. 11:42 Barney: Some of these reports will include the comparison of actual versus budgeted values, and any key variances will be used to revise or amend the budgets/forecast plans. We return to where we started with a review or modification of our strategic financial plans. 11:59 Nikita: Barney, what are the key job roles associated with the Budget to Report process? Barney: There are three job roles associated with this process that are predefined as standard by Oracle: General Accountant, Financial Analyst, and General Accounting Manager. The General Accountant manages all financial transactions and revenue, expenses, assets, liability, and equity accounts, and is responsible for recording accounting adjustments, such as accruals, allocations, currency revaluations, and translations. The Financial Analyst analyzes the financial performance of an enterprise or an organization. The General Accounting Manager manages the general accounting functions of an enterprise, including general ledger, subsidiary ledgers. They also manage period close activities. 12:49 Lois: Any final words, Barney, as we conclude this series on ERP Financials business processes? Barney: So, in these last couple of episodes, we discussed the five financial business process life cycles. These processes are collectively known as Record to Report. The Record to Report process includes data extraction, collection, and processing to deliver accurate and timely financial information and enhance decision-making within the organization or enterprise. Using embedded analytics to drive an error-free financial close process, Oracle Fusion Cloud can not only automate and transform the R2R process, but also enable timely, real-time financial performance reporting. 13:37 Nikita: Thank you so much, Barney, for being our guide and taking us through the Oracle Financials Business Process Model. Barney: Thank you. It’s been great being here with both of you. Lois: If you missed any of our earlier episodes with Barney, you should go back and check them out. And if you’re interested in learning more about Oracle’s business process training and getting certified, visit mylearn.oracle.com. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 14:03 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/28466369

The Procure to Pay and Asset Acquisition to Retirement Process Flows 11/07/2023

The Procure to Pay and Asset Acquisition to Retirement Process Flows Time to unlock your finance team’s digital potential! Join Lois Houston and Nikita Abraham, along with Sr. Principal ERP Learning Strategist David Barnacle, as they discuss the Procure to Pay process flow, an integral financial process that integrates purchasing with accounts payable activities. They also talk about the Asset Acquisition to Retirement process flow, which includes all the main activities that occur during the life of an asset, right from acquiring it all the way to disposing it at the end of its life. Oracle MyLearn: Oracle University Learning Community: LinkedIn: X (formerly Twitter): Special thanks to Arijit Ghosh, David Wright, Radhika Banka, Parvathy Narayan, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone. In our last episode, we looked at Invoice to Cash, which is the first business process within the Oracle Cloud Financials Business Process Model. Invoice to Cash refers to everything from the moment a receivable invoice is created until the customer's receipt is settled and reconciled with the bank statement. If you missed that episode, do go back and give it a listen. 01:01 Lois: Today, David “Barney” Barnacle, our Sr. Principal ERP Learning Strategist is back on the podcast to tell us about the next two business processes, Procure to Pay and Asset Acquisition to Retirement. Nikita: Hi Barney! Thanks for being back with us. So, what is the Procure to Pay process? 01:20 Barney: Hi Niki. Hi Lois. Good to be here. Let’s focus on Procure to Pay or P2P for short, which is an integral financial process within any organization. It integrates purchasing with accounts payables activities and involves a series of tasks. These could include placing a purchase requisition or a purchase order, receiving and inspecting the delivered goods or services, capturing the supplier invoices, which is the company’s liability to the supplier, matching the unit quantity and price to the original PO, calculating the relevant taxes or withholding taxes, approving the charges for final payment by the company, and finally, recording the bank statement lines with all payments made to suppliers or employees. 02:04 Barney: Oracle Fusion Cloud: ERP's capacity to use cutting-edge technologies for effective operations is what distinguishes it from the competition. The true value is in the automation, which helps enterprises improve processes, increase efficiency, and get the latest insights and alerts. Let me give you some examples within P2P. We have something called Intelligent Document Recognition or IDR for short, which is a fully integrated invoice recognition solution. As you know, many suppliers send payables invoices electronically via email. With Oracle’s cloud solution, IDR extracts invoice information from the emailed documents to create invoices and then imports them directly into Payables. Another interesting feature is the ability to calculate the trip distance in mileage expense entry by using the Oracle Maps Cloud service. For mileage expense types, on the Oracle Maps page, you can enter the start location, subsequent stops, and the end location. It’s that easy. 03:09 Lois: Oh wow, that’s pretty cool. I remember having to track my miles manually many years ago. What a nice feature. So, is creating an invoice the beginning of the P2P process? Barney: No, Lois. Invoicing is not the beginning but just one part of the larger parent P2P process. The P2P process can be broken into three key phases. These phases are set in a repeating loop and fine-tuned and improved with every cycle. The three phases are the Purchasing process, the Receiving process, and finally, the Payment process. 03:46 Barney: During the Purchasing process, purchase requisitions for goods and services are created and approved. Suppliers are evaluated and selected. And then, purchase orders are issued for the required goods and/or services. Next is the Receiving process, where goods and services are received. Receiving documents are then reviewed and logged for the goods. In the final Payment process, which includes the Invoice, Payment, and Reconciliation sub-processes, invoices are received and invoice processing is completed, recording the supplier’s liability. Invoices are reconciled and cross-checked with the original purchase orders and goods receipts or receiving documents. This is called purchase order or receipt matching, and it ensures that the enterprises only pay for goods and services it has ordered and received. Errors are recorded and corrected, and approved invoices are paid, reducing the supplier’s liability. Payments are then reconciled with bank statement lines. 04:47 Nikita: OK. So, there are multiple activities, like purchasing, receiving, and invoicing, which are part of the P2P process. But how do these activities flow with regard to the Oracle Fusion Cloud: ERP application? Barney: The Procure to Pay process spans multiple departments within an organization. And in the Oracle application, it covers different modules like purchasing, payables, cash management, and general ledger. Demand generation for goods or services can originate in the Manufacturing departments based on planned or actual orders, or by internal employee orders for goods or services that the business requires. 05:23 Barney: This demand gets converted into requisitions within the Purchasing department. Everything from the creation all the way to the authorization of these requisitions is performed within the Purchasing department. Once the requests have been authorized, the buyers or procurement agents consolidate the requests and convert them into a purchasing document, like a purchase order. Next, the process of receiving goods or services against the purchasing document is typically carried out by the employees requesting those goods or services or by the staff at the receiving location. 05:54 Nikita: And a receiving location could be a warehouse, for example, right? So there is a purchasing department processing purchased orders and another receiving department recording the receipt of goods. Barney: Exactly, Niki. And once the goods are received and recorded, the transaction flows into the Payables department within the finance business function. Supplier Invoice to Payment, which comes within the finance business function, touches internal as well as external parties involved with an organization. 06:23 Barney: For example, when talking about the process of expense reimbursement, employees are considered internal parties. In this process, employees record and submit expenses incurred on behalf of the organization and are reimbursed for the authorized items. For an external party, the process of recording invoices against goods or services used by the organization and the subsequent process of making payment to clear these invoices is also a key part of the Procure to Pay process. For example, organizations purchase assets like printers or furniture, which are recorded as part of the purchasing process. Oracle Assets is a fully integrated solution to track internal products and assets at internal or external sites, while providing the ability to capture financial transactions with back-office automation. 07:10 Barney: And then there is the Treasury department that some companies may call the Cash department, which, at periodic intervals, receives bank statements and reconciles the statement lines with payments made to suppliers. The key focus of the Treasury department is to determine the cash position and to assist in managing the cash forecasting process. These are just some activities of the Procure to Pay process that touch multiple departments within the business. 07:35 Lois: You mentioned expense reimbursement and making payments to external suppliers for goods received. How does this fit into the Oracle Financials Business Process Model? Barney: As we discussed, P2P involves multiple processes spanning procurement to cash, specifically payables invoice to cash. Let me list out the processes that are aligned with Oracle Financials business processes as part of the P2P process. First, we have the Expense Report to Reimbursement process that deals with getting business expenses reimbursed for employees and contract workers, also known as contingency workers. 08:13 Barney: The Supplier Invoice to Payment process deals with recording liability for purchases made by the organization directly from external parties and paying for those purchases. The Capture Tax process deals with applying transactional tax or withholding taxes based on the information entered in an invoice and invoice line level, and legislative requirements. The Bank Transaction to Cash Position process deals with matching bank statement lines to payments made to suppliers. Accountants working in the Treasury department can prepare the expected cash position based on the expected receipts and payments within that specific period. 08:51 Lois: Each of the Oracle business processes you mentioned seem to be aligned with the general flow of activities in a typical organization. What are the advantages of having such a streamlined P2P process? Barney: A streamlined and automated Procure to Pay process helps organizations remain compliant with supplier-related contractual terms and legislative tax requirements. It also helps them reduce the risk of fraud with risk migration controls in place and automation within the process. 09:18 Barney: It results in better supplier management in terms of sourcing and evaluating suppliers, and monitoring and controlling supplier invoice aging, resulting in timely payments being made to suppliers. The ability to capture supplier invoices from multiple channels, including scanning and online submission by suppliers to enable batch processing of payments, results in cost reduction for an organization and saves hours that would have been spent manually processing invoices and payments. Most importantly, a streamlined Procure to Pay process provides the ability to capture data at each stage, which helps with future decision-making. 09:56 Nikita: What are the job roles associated with the Procure to Pay business process? Barney: There are a few key job roles in the P2P business process. There’s the employee job role, which identifies the person as an employee who can create a requisition and an expense report. The Procurement Agent job role is responsible for transactional aspects of procurement processing. The Expense Audit job role reviews and audits expense reports daily to ensure compliance with the company's reimbursement policy. The Accounts Payable Specialist job role enters invoices, ensuring accuracy, uniqueness, and completeness, and matches invoices to the correct purchase orders or receipts, all while making sure that the invoices comply with company policy. The Accounts Payables Supervisor job role oversees the activities of Accounts Payable Specialists, initiates and manages payment runs, and resolves non-data entry holds. And finally, the Cash Manager job role protects and develops the company's liquid assets, maximizing their use and return to the organization. 10:58 Lois: For an organization to have an optimized Procure to Pay process, I’m sure they need to track certain key performance indicators, right? Barney: Yes, and they do. Some of the KPIs that are tracked for the P2P process are Expenses vs. Budget, Invoice Payment Days, % Discount Taken, Time to Settlement, Time to Reconcile, and Payables Overdue Invoices. 11:20 Lois: Barney, earlier you spoke about how easy it is to raise expenses and use the Maps functionality. Are there other emerging technologies used by the ERP application in the Procure to Pay process? Barney: Yes Lois, Oracle Fusion Cloud: ERP uses the latest emerging technologies like artificial intelligence, digital assistants, and image scanning in different areas of the Procure to Pay process. Adaptive Intelligence models are used in the Payables module to calculate and recommend discounts for single payments. Intelligent Document Recognition is used to scan and automate the invoice creation process in Payables, incorporating the required reviews and approvals. Within the Expenses module, Digital Assistants are used to punch in expenses and submit them automatically. You can also click photos of receipts and process them to input the required expenses. 12:16 Working towards an Oracle Certification this year? Grab all the help you can get. Attend a cert prep live event in the Oracle University Learning Community. And once certified, don’t forget to join our exclusive forum for Oracle certified users. If you’re already an Oracle MyLearn user, go to MyLearn to join the community. You will need to log in first. If you’ve not yet accessed Oracle MyLearn, visit mylearn.oracle.com and create an account to get started. 12:47 Nikita: Welcome back. Barney, you mentioned that Payables is one of the starting points to capture or track assets in the application. Can you help us understand how this is built in to Oracle Fusion Cloud? Barney: In the Oracle Financials Business Process Model, Asset Acquisition to Retirement is a key process that covers all the main activities that occur during the life of an asset, anything from acquiring it to disposing it at the end of its useful life. There are many things a business will need to do with assets. Capture asset acquisition, record financial transactions, track asset movement for reporting and regulatory purposes, and so on. We can manage these assets and simplify fixed asset accounting tasks with the help of Oracle Fusion Assets. It has the ability to record leased assets in line with the requirements of the two new accounting standards. 13:37 Barney: Oracle Assets integrates with other modules like Payables, Subledger Accounting, and Projects. You can add assets and cost adjustments directly into Assets from invoice information in Payables. The Create Mass Additions for Assets process sends valid invoice line distributions and associated discounts from Payables to the Mass Additions interface table in Assets. You can then review the mass addition lines in Assets and determine whether to create assets from them. 14:03 Nikita: So, what are the different stages in the Asset Acquisition to Retirement life cycle? I’m sure the first one has to be acquiring the asset. Barney: You’re absolutely right there, Niki. The Asset Acquisition to Retirement life cycle starts with the Asset Acquisition stage. A business can acquire an asset through the Procure to Pay life cycle and record the asset in the asset register. An asset can be acquired by purchasing it, leasing it, constructing/developing it (i.e. by the use of Oracle Fusion Projects), or by mergers or acquisitions. And by acquiring assets, we mean capturing and recording the purchase of assets from all business locations. 14:42 Barney: The next stage is Monitoring and Tracking. Once an asset has been created and added to the asset register, you can perform various activities during the asset’s life cycle. These activities could be changing its category or financial details, transferring assets, i.e. from locations, or running or changing depreciation. Any finally, we have Retirement. When you sell an asset, or an asset is lost, or the asset reaches the end of its useful life, you must remove it from the asset register. 15:14 Lois: And before we let you go for today, remind us – what job roles perform the functions related to this life cycle, Barney? Barney: There are two main job roles involved in this process. One is the Asset Accountant, the basic user who performs all functions in the asset management module. Then there’s also the Asset Accounting Manager who has much of the same access as the asset accountant, along with extra access in terms of reporting and running accounting processes. 15:40 Nikita: I think we’ve discussed these two important Oracle business processes quite thoroughly. Thank you so much, Barney, for taking us through them. Barney: Thanks for having me! Lois: Yes, thanks, Barney. This is a great introduction. Next week will be our final episode on the Oracle Financials Business Process Model, where we’ll cover the Accounting Transformation and Budget to Report business processes. And don’t forget to head over to mylearn.oracle.com to learn more about these processes and get certified. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 16:13 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast. /episode/index/show/oracleuniversitypodcast/id/28466312

The Invoice to Cash Process Flow 10/31/2023

The Invoice to Cash Process Flow Want to know about the key financial business processes that make up Invoice to Cash? Lois Houston and Nikita Abraham, as well as Sr. Principal ERP Learning Strategist David Barnacle, are here to simplify this critical process flow for you. In this episode, they go over the entire Invoice to Cash process flow, which includes everything from the moment the invoice is created to the moment when the customer's debt (payment) is settled and reconciled with the bank statement. Oracle MyLearn: Oracle University Learning Community: LinkedIn: Twitter: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, Parvathy Narayan, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hello everyone! In our last episode, we spoke about Enterprise Resource Planning business processes, particularly those related to Oracle Fusion Cloud: Financials, with our Sr. Principal ERP Learning Strategist David “Barney” Barnacle. We discussed how there are five business processes within the Oracle Cloud Financials Business Process Model. Today, Barney joins us once again to take us through the first of those business processes, the Invoice to Cash process. Nikita: Welcome back, Barney! 1:08 Barney: Hi Niki. Hi Lois. Nikita: Barney, what does the Invoice to Cash business process cover? Barney: Invoice to Cash is a child process of a parent life cycle commonly known as Order to Cash. Order to Cash includes all the steps involved in fulfilling customer orders, from order entry to delivery to the final customer payment. 01:31 Barney: Order processing can take many forms depending on the industry, product, and customer. It can range from delivering standard items that are directly shipped from stock to complex items, configurations (or structures), which can be fulfilled from multiple sources i.e. make, buy, or transfer. It can include processes such as drop shipments and internal orders. Certain businesses may process orders based on subscriptions only, which may or may not include fulfilment of items. If you’re interested in learning more about these complex business subprocesses, I’d suggest visiting mylearn.oracle.com and looking for the business processes under Supply Chain Management (SCM), in particular Order Management Processing. 02:20 Barney: Here, in the business process for financials, we have simplified Order to Cash into two child subprocesses: Order to Shipment and Invoice to Cash. It is the second subprocess i.e. Invoice to Cash that uses financial products and covers customer billing (including the calculation of transaction tax), customer payments, also known as receipts, bank statements, reconciliation of receipts, and the ultimate creation of accounting entries for all transaction events in this billing process. 02:55 Lois: So, you’re saying Invoice to Cash is just one part of the Order to Cash process. Barney: That’s right, Lois. While the origin or source of a customer transaction can be multiple feeder systems (for example, Order Management, Projects, Subscription Management, and third-party or legacy billing systems), Invoice to Cash refers to an end-to-end process covering everything from the moment an invoice is created until the customer's debt is finally settled and reconciled with the bank statement. The real value for businesses lies in automating the process and getting insights and alerts from the Oracle Cloud applications to improve their overall profitability and cost savings. 03:38 Lois: Help me understand the flow of events, Barney. Because surely there are processes that occur before an invoice is raised, right? What are the processes covered in the larger Order to Cash cycle? Barney: You’re absolutely right, Lois. Let’s break it down further. Order to Cash is the parent business process. It starts in Order Management (with order capture and pricing) and ends in Cash Management (with the reconciliation of customer receipts). If we take a simple view of Order to Cash, we can use, as our example, ordering standard product items delivered directly to the customer from existing stock. We have two subprocesses here: Order to Shipment and Invoice to Cash. These processes use many different SaaS products. 04:25 Barney: The Order to Shipment subprocess starts with order capture by the order entry clerk, the salesperson, or directly input by the customer. The order captures essential attributes, such as items and quantities, required delivery dates, and financial contract terms, like payment terms, and so on. The pricing engine is called to create a sales price and then the global order promising check verifies supply of the items. Once the order is validated, submitted, and optionally approved, the order line passes on to order orchestration or fulfillment. 05:04 Barney: The order orchestration process drives scheduling and reservations. Then, within warehouses, the items are picked, packed, and shipped to the customer. Once the shipment is confirmed, the customer is invoiced based on contractual terms. Here, the second subprocess of Invoice to Cash takes over. The order orchestration process pushes the order attributes into the auto invoice interface tables. From there, the Billing Manager runs auto invoice to import customer invoices. This, in practice, will often be automated. The transactions will include the correct taxes as well as default accounts, and revenue will be recognized based on defined revenue recognition rules or events. 05:52 Nikita: Can I just interrupt, Barney? What do you mean by revenue recognition rules? Barney: Revenue recognition is an accounting principle that asserts that revenue must be recognized as it is earned. Let’s look at this simply. The revenue recognition principle is a key component of an . This accounting method recognizes revenue once it is considered earned, unlike the alternative cash-basis accounting, which recognizes revenue at the time cash is received or anytime cash changes hands. In the case of , the revenue recognition principle is not applicable. 06:34 Barney: Revenue is generally recognized after a critical event occurs, like the product being delivered to the customer. Revenue recognition standards can vary based on a company’s accounting method, geographical location, whether they are a public or private entity, and other factors. In essence, revenue recognition looks to answer when a business has earned its money. Typically, revenue is recognized after the performance obligations are considered fulfilled, and the currency amount is easily measurable to the company. A performance obligation is the promise to provide a distinct good or service to a customer. On the surface, it may seem simple, but a performance obligation being considered fulfilled can vary based on several factors. 07:19 Barney: Essentially, the revenue recognition principle means that companies’ revenues are recognized when the service or product is considered delivered to the customer — not when the cash is received. Determining what constitutes a transaction can require more time and analysis than one might expect. To accurately recognize revenue, companies must pay attention to the five steps outlined in the various accounting standards and ensure they are interpreting them correctly. 07:47 Barney: For revenue recognition within our simple process flow, we could use an account receivables invoice and accounting rule to defer revenue, or we could pass the information over to the Revenue Management product to follow the steps of the relevant accounting standards and only recognize earned revenue when a performance obligation has been satisfied. Nikita: OK, I get it now. Thanks for that, Barney. 08:10 Barney: Great. So, getting back to our financial process, the invoice or invoices are either printed or electronically sent to the customer. The payment terms attached to each transaction will determine when full payment is due and may include early settlement discounts. Monthly statements sent to the customers will highlight account balances and any late or overdue transactions. Customers will send their payments, manually or electronically, and the company may also create automatic receipts (commonly known as direct debits) to transfer funds from customer bank accounts to the company’s bank account on a regular monthly basis. 08:52 Barney: The receipt received will be applied to the open transactions (debit items such as invoices) and either clear or reduce the customer’s account balances. The cashier will then ensure these receipts have all been correctly accounted in the company’s bank account – a step called bank account reconciliation. The subledger accounting rules engine will ensure that at each transaction event (e.g., create invoices, adjust invoices, create receipts), the correct accounting is created and ultimately transferred to the general ledger as receivables journals. That means a full account record is created for each order line processed within the Order to Cash flow. 09:37 Barney: Finally, the Collections team monitors customer account balances on a regular basis and with various collection strategies and actions (such as sending dunning letters) aims to reduce Days Sales Outstanding and improve the company’s cash inflow. 09:53 Lois: Let me make sure I get this. We have the larger life cycle, the Order to Cash process, which connects the various pillars of Enterprise Resource Planning, or ERP, like Financials and Procurement. And within them, there are modules like Order Management, Receivables, Collections, Cash Management, and General Ledger. Barney: Exactly, Lois. Nikita: So, since the focus of this series is on Oracle Financials, we’d like to learn more about the processes under it. 10:19 Barney: OK, Niki. Oracle Cloud provides capabilities to streamline the Invoice to Cash business process, and Oracle Receivables Cloud is the cornerstone of the Invoice to Cash solution. This application helps you improve cash flow, increase efficiencies, and optimize customer relationships. It has user-friendly interfaces that you can leverage to efficiently manage the process. And you can proactively manage the entire customer billing cycle and process customer receipts. 10:51 Nikita: From what I understand, the Accounts Receivable Specialist seems to be an important role in the Order to Cash process. So, how does the Oracle application help Receivables Specialists work more efficiently? 11:02 Barney: Oracle Receivables has embedded business intelligence that offers summarized dashboards within the work areas, giving you or giving the receivables specialist an intuitive, simple, and modern user experience. Infolets highlight, in real-time, issues with the key processing steps, such as auto invoicing, receipt processing, etc., allowing receivables specialists to take effective action. Some of these errors can also be downloaded into a spreadsheet for efficient bulk correction of data. 11:40 Barney: Another interesting feature is social enterprise network, which can highlight issues within the receivables and collections team, leading to quicker adjustments or corrections of the customer account balances or transactions. There’s also Oracle Bill Management, which provides a self-service approach to reduce customer inquiries. You can set up Bill Management to enable the customer to directly complete various receivables processes for themselves, such as reviewing outstanding transactions and credit memos, monitoring disputes, and more importantly, making online payments. 12:22 The Oracle University Learning Community is a great place for you to collaborate and learn with experts, peers, and practitioners. Grow your skills, inspire innovation, and celebrate your successes. The more you participate, the more recognition you can earn. All of your activities, from liking a post to answering questions and sharing with others, will help you earn badges and ranks, and be recognized within the community. If you’re already an Oracle MyLearn user, go to MyLearn to join the community. You’ll need to log in first. If you’ve not yet accessed Oracle MyLearn, visit and create an account to get started. 13:01 Nikita: Welcome back. So Barney, you spoke about how Invoice to Cash has several tasks within it, like invoicing customers, collecting payments, and so on. How does all of this come together in terms of the Oracle Cloud Financials Business Model? Barney: Invoice to Cash is an integral financial process within organizations and is broadly divided in our model into four subprocesses: Customer Invoice to Receipt, Capture Transactions, Customer Statement to Collection, and Bank Transaction to Position. Let’s have a look at each of these in turn. 13:36 Barney: The Customer Invoice to Receipt subprocess includes several tasks. Everything from recording the invoice to be sent to customers for goods sold or services provided and addressing billing-related issues, if there are any, to recording customer receipts, making adjustments to outstanding amounts, posting receivables activities so that the Receivables subledger can be seamlessly closed, and finally using analysis and reporting tools to get deeper insights and drive better decision-making. 14:04 Lois: That’s a lot of details that are being captured. Barney: Yes, Lois. Every minute detail that affects the financial status of an organization can be captured, like the Capture Taxes subprocess. This is the process of applying required taxes based on legislative requirements. It’s based on the information entered within the invoice and invoice line level. This could be regarding customer ship to, bill to, product and tax classification codes, and so on. The system automatically applies the attributes and calculates the correct taxes at the invoice line level and then calculates the total taxes applicable to the whole invoice. 14:43 Barney: Then we have the Customer Statement to Collections subprocess, which includes sending statements to customers at periodic intervals, flagging delinquents, creating and assigning collection-related tasks to collection agents, recording and resolving disputes raised by customers, recording payments, and tracking and measuring KPIs to review the collection team’s performance. And finally, the Bank Transaction to Cash Position subprocess deals with matching bank statement lines to payments received from customers. Accountants working in the Treasury Department can prepare the expected cash positions, based on the expected receipts and payments to be made within the specific time period. 15:26 Lois: OK, so we’ve established that the application captures a lot of details. But we also need to be able to extract this data to assess the financial health of the organization, right? So, when it comes to receivables activities, what are the key performance indicators for an organization? Barney: Yes, you’re right there, Lois. KPIs are required to closely monitor and measure the performance of an organization. And to really optimize the Invoice to Cash process, the Receivables department in any organization will have certain KPIs they need to track. 16:01 Barney: Some critical ones we’ve already mentioned are Days Sales Outstanding or DSO, which measures the average number of days that a company takes to collect revenue after a sale has been made, Time to Settle, Percentage of Current Receivables, Average Invoice Age, % Disputed Invoices, Operational Cost Per Collection, Number of Delinquent Accounts, and Time to Reconcile. These are all important KPIs. All these KPIs are easily available in the Oracle application in a visual representation, like a graph or percentage, and can be viewed by management simply in a single dashboard. They can also be displayed in a user-designed format for greater efficiency. 16:49 Nikita: Thank you so much, Barney, for coming back to talk to us about the Invoice to Cash business process. Barney: No worries. Happy to be here. 16:56 Lois: We’re really looking forward to having you back next week to tell us about the next two business processes, Procure to Pay and Asset Acquisition to Retirement. And if you want to learn more about these ERP business processes and get certified, visit mylearn.oracle.com. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 17:18 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. 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An Overview of ERP Business Processes 10/24/2023

An Overview of ERP Business Processes Inside every successful corporation is a well-oiled financial team working to do what is in the best interest of the company. From the CFO to the finance intern, having a system of modern best practices and a comprehensive suite of financial solutions, properly utilized, is more important than ever before. Join hosts Lois Houston and Nikita Abraham, along with Sr. Principal ERP Learning Strategist David Barnacle, as they introduce you to the Oracle Cloud Enterprise Resource Planning suite and the key business processes within it. Oracle MyLearn: Oracle University Learning Community: LinkedIn: Twitter: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, Parvathy Narayan, and the OU Studio Team for helping us create this episode. --------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone! A few weeks ago, we concluded our spotlight on HCM business processes. And today, we’re going to move on to ERP business processes. ERP, as you may already know, is Enterprise Resource Planning. And to take us through this, we have David “Barney” Barnacle, our Sr. Principal ERP Learning Strategist. For over 20 years, Barney has successfully trained customers in the rapid configuration and effective use of Oracle Applications. Welcome Barney! We’re so glad to have you here today. 01:10 Barney: Thank you! Happy to be here. Lois: In previous episodes this season, we’ve spoken about the business process training that Oracle has. But from an ERP point of view, can you briefly tell us what it is and why we have it? Barney: Sure, Lois. Inside every successful corporation is a well-oiled financial team working to do what is in the best interest of the company. From the CFO to the financial trainee, everybody within an agile, innovative enterprise will be using modern best practice processes within a complete and fully integrated suite of SaaS applications. That’s why we have Oracle Business Process Training for Oracle Fusion Cloud: ERP. It’s a powerful training solution that gives users and companies a solid understanding of how Oracle ERP solutions work together to ensure effective and efficient transaction processing. 02:06 Nikita: Now that we know why we have this training, let’s get down to it. Barney, what is ERP? Barney: Enterprise Resource Planning or for short as it’s known, ERP, is a system or a collection of systems that companies use to manage day-to-day business activities efficiently. The ERP system brings together different dimensions of businesses from financials, procurement, risk and compliance, to enterprise performance management, and Oracle Fusion ERP analytics. ERP systems tie together several business processes and seamlessly allow data to flow between them. By collecting an organization’s shared transactional data from multiple sources, ERP systems eliminate data duplication and provide data integrity with a single source of truth. 03:00 Barney: Today, ERP systems are critical for managing thousands of businesses of all sizes and across all industries. For these companies, ERP is as indispensable as the electricity that keeps the lights on. Remember, simply, ERP is the vehicle for integrating people, processes, and technologies across a modern enterprise. 03:27 Lois: Barney, in the world of various ERP systems, how is the Oracle Cloud ERP application suite unique? And how is it structured? Barney: Thanks, Lois. Oracle Cloud ERP is a cloud-based SaaS (Software as a Service) application, or group of applications, that provides end-to-end business solutions. These solutions, in turn, enable businesses to significantly cut down costs, improve their process efficiencies, and enhance collaboration. Oracle Cloud ERP is classified into six different product families. We have Financial Management, Procurement, Project Management, Risk and Compliance, Enterprise Performance Management (EPM), and finally Enterprise Resource Planning Analytics. 04:14 Lois: So as I understand it, we have Oracle Cloud ERP, a modern cloud-based software application, and then have Oracle Financials, Procurement, EPM, and so on within the Oracle Cloud ERP suite, right? Barney: Correct. That’s right, Lois. Nikita: That’s quite an interesting mix of product families. Can you tell us about each of these products, Barney? 04:35 Barney: Sure can. Let’s start with Financials. Oracle Cloud Financials is a global financial platform that connects and automates your financial management processes, including payables, receivables, fixed assets, expenses, GL, and reporting, for a clear view into your total financial health. 04:56 Barney: Next is Oracle Cloud Procurement, an integrated source-to-settle suite that automates business processes, enables strategic sourcing, improves supplier relationship management, and simplifies buying. Then we have Project Management. Oracle Project Management helps you plan and track your projects, assign the right talent, balance capacity against demand, and scale resources up or down quickly as needs change. 05:27 Barney: Then there’s Risk and Compliance. Oracle Cloud Risk and Compliance Management is a security and audit solution. It controls user access to your Oracle Cloud ERP financial data, monitors users’ activity, and makes it easy to meet compliance regulations through automation. 05:45 Nikita: Interesting. And these pillars are connected to one another? Barney: Yes, Niki. One of the biggest advantages of choosing the Oracle Cloud ERP solution is its ability to integrate all the product offerings that come under it. Now, getting back to the product pillars in the ERP suite, we have Enterprise Performance Management. With this, you can model and plan across Finance, HR, Supply Chain, and Sales. You can streamline the financial close process and drive better decisions. 06:18 Barney: And closing out the list, we have ERP Analytics. Oracle Fusion ERP Analytics is a prebuilt cloud-native solution for Oracle Cloud ERP. It helps financial and procurement professionals uncover underlying drivers of profitability, improve the use of working capital, and control business expenditures. Oracle’s unique application capabilities enable organizations to unify financial analytics across different departments. So those are the six product families that make up the Oracle Cloud ERP. 06:56 Lois: I think that’s what all organizations want, right? A comprehensive solution to help them meet their business needs. But how do organizations that have legacy ERP systems and processes move to the Oracle Cloud ERP systems? Do they need to completely switch over? And what happens to the existing processes they’ve been following? Barney: Yes Lois, they can co-exist. But you can implement just one cloud product, such as expenses or revenue management, to enhance the existing or current business processes. You can then have several step-by-step projects to move different business processes to the cloud – until you are 100% cloud. 07:37 Nikita: So, within the ERP suite, we’d like to learn more about Oracle Cloud Financials. Can you tell us about the types of organizations that would benefit from using Oracle Cloud Financials? Barney: Sure, Niki. So like I was saying before, Oracle Cloud Financials gives you a complete view of the overall financial health of your organization by connecting data and process automation in payables, receivables, fixed assets, expenses, GL, and reporting. It also helps organizations respond quickly to an everchanging environment and deliver decisive insights as needed. It is a comprehensive global solution designed for organizations of every size and industry. 08:20 Lois: Barney, a typical organization will have various transactions and processes that have a direct impact on their financial balance, right? Everything from financial accounting, transaction processing, payment delivery, and cash reconciliation, to employee expense processing, and even asset management. How does Oracle Cloud Financials process all these activities or functions? 08:41 Barney: Good question, Lois. Oracle Cloud Financials has several modules to help businesses handle all the processes you’ve just mentioned. Let’s say procuring items, creating invoices, and making payments to suppliers are the typical day-to-day activities of a manufacturing organization. In Oracle Cloud Financials, the process of invoice to payment is handled by Oracle Payables and Payment tasks, which provide streamlined and user-friendly interfaces for Payables professionals to efficiently manage the Supplier Invoice to Payment process. 09:18 Barney: Let me give you another example. Every organization will have employees submitting their expenses for approval and payout. These expense-related activities are handled by Oracle Expenses. Oracle Expenses gives your organization the tools it needs to effectively manage the travel and expenses process. You can simply start the whole process by uploading a copy of your expense receipt using the expenses mobile app. 09:46 Lois: So, it looks like every financial end-to-end process in an organization is represented in Oracle Financials. Barney: Yes. Let’s take Oracle Receivables. It provides streamlined and user-friendly interfaces for Receivables professionals to efficiently manage the credit-to-cash process. You can use it to proactively manage the entire cycle of billing customers and processing customer payments. 10:11 Barney: When you deal with cash that needs to be received from customers in a very large organization, it’s important that you understand these customers well and build a good relationship with them. And to do this, many organizations hire specialists called collection agents. Oracle Advanced Collections is designed for these collection agents. For anyone who doesn’t know, collection agents are basically those responsible for working with late or nonpaying customers to resolve payment issues and remove non-payments. 10:43 Nikita: That can be quite problematic, right? I’m sure any delays in payment from customers would cause cash flow problems for a company. Barney: Exactly. And so Advanced Collections is the critical final step in the credit/billing-to-collections business process, where the aim is to reduce delinquent customers, encourage prompt customer payment, and minimize the company Days Sales Outstanding. For those who don’t know, Days Sales Outstanding is the average number of days it takes a company to receive payments for a sale. 11:19 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all of which is available free to subscribers. So, get going! Pick a course of your choice, get certified, join the Oracle University Learning Community, and network with your peers. If you’re already an Oracle MyLearn user, go to MyLearn to begin your journey. If you have not yet accessed Oracle MyLearn, visit and create an account to get started. 11:58 Nikita: Welcome back. We were just going through the various modules within Oracle Financials. So far, we’ve covered Payables and Payments, Expenses, Receivables, and Advanced Collections. What other modules are there, Barney? Barney: We have Oracle Assets, which is a complete asset management solution. Nikita: So assets like property… equipment… Barney: Exactly. Yes, and Oracle Assets helps you maintain your assets accurately. It supports the end-to-end asset life cycle from asset acquisition to retirement, i.e. from capture to retire. Also, if you do not directly acquire or buy the assets outright, we have a product called Lease Accounting, which covers all the accounting entries for leased assets. 12:45 Nikita: Ok. And what else? Barney: We also have Oracle Cash Management, tax engines, subledger accounting, intercompany engines, and general ledger. Nikita: Can you quickly run us through these modules? Barney: Sure. Oracle Cash Management is a comprehensive business solution that helps you optimize enterprise-wide liquidity and control cash. Tax engine is used to calculate transactional taxes based on business, products, places, and country rules. For example, when to use sales tax as opposed to when to use value added tax or VAT. 13:19 Barney: Oracle Subledger Accounting is a powerful, rules-based accounting solution that provides consistent accounting treatment for financial transactions created across Oracle Cloud applications. The intercompany engine is used to automatically balance journals that are between different organizations. For example, a central management fee charged annually between the organization’s headquarters and each of its subsidiaries. In the cloud, with intercompany, you can have up to three balancing segments. However, the primary balancing segment is still used to identify the company or fund holder. 13:59 Barney: Then we have Oracle General Ledger. Oracle General Ledger is the central repository of accounting information. It provides highly automated financial processing, effective management control, and real-time visibility into financial results. 14:16 Lois: Before you continue, Barney, can you tell us what a general ledger system does? How is it different from subledger accounting? Barney: Sure, Lois. The main purpose of a general ledger system is to record the financial activities of a whole company and produce financial and management reports to help people inside and outside the organization make decisions. 14:38 Barney: Subledger journals can provide the detail and a fully auditable path back to the source transaction that created the accounting entry. This single source of subledger accounting detailed data is often summarized into account balances when transferred across to general ledger. It is these summarized GL account balances that will drive real-time financial reporting using the various cloud reporting tools. 15:04 Lois: Ok. So, you’ve told us about the modules within the Oracle Cloud Financials suite that capture different types of transactions. For Oracle Fusion Cloud users, what’s the quickest way to uncover business insights and deliver financial improvements to their enterprise? Barney: To identify underlying drivers of profitability, improve the use of working capital, and control business expenditures, we have Oracle Fusion Cloud ERP Analytics. This is an analytics-based, state-of-the-art, technology-driven tool that provides you with the organization’s financial status at any given point. Financial Reporting and Analytics helps you speed up your decision-making process with graphical insights, configurable analytics dashboards, and event-based delivery of information so you can take targeted actions. 15:56 Lois: That’s interesting. Thanks for that, Barney. That was a lot for me to take in, but it’s very insightful. Nikita: Yes, it was. Getting back to some of the processes you mentioned, like the Expense Report process or the Supplier Invoice to Payment process, can you give us an overall picture of how these processes are fulfilled within the Oracle Cloud Financials function? 16:17 Barney: Absolutely. At Oracle, we can map these different processes to the Oracle Financials Business Process Model. This model refers to the end-to-end business processes enabled by Oracle applications to record financial data and derive reports for efficient and successful business performance. There are key business processes in this model. 16:42 Nikita: I know you’re going to be joining us in upcoming episodes to talk about each of these business processes, but can you briefly tell us about them? Barney: Sure, Niki. Let’s start with the process that records cash inflow into an enterprise. Invoice to Cash refers to the cycle of events captured in the system, starting with recording customer invoices for goods and services provided, processing customer receipts based on the terms of payment, and reconciling receipts with bank statements and customer account balances with the Account Receivables ledger balances. 17:15 Barney: Then, we have the major processes that cause an outflow of cash from the enterprise. Procure to Pay involves recording the journey of transactions, from requisitioning and procuring goods and services from the supplier to receiving them at their destination and the ultimate entering of supplier invoices and payments. Asset to Retirement empowers organizations to manage the entire financial cycle of assets, including acquisition, capitalization, depreciation, and the ultimate retirement. 17:46 Barney: Accounting Transformation is the process of converting transactions or activities from subledgers to journal entries to meet corporate needs and statutory or legal reporting. And finally, Budget to Report covers the entire process of creating budgets, capturing actuals, and conducting budget reporting and variance analysis. 18:08 Lois: Each of these business processes has users with various job roles working on the application, right? They’ll be taking action… providing input… So how does Oracle Cloud Financials define these job roles in the application? Barney: Well, in small or medium-sized companies, the financial functions may be the responsibility of a single accountant. But in large organizations, there is going to be a financial team, where the job roles are specialized, often focused on a particular business task or function. 18:43 Barney: The responsibility for these roles are often grouped into three main categories. We have the Specialist or Clerk, who is responsible for a specific group of tasks. For example, creating payments or reconciling bank statements. Then we have the Supervisor, who is responsible for managing the specialist executing the specific business tasks. And finally, we have the Managers or Manager, who is responsible for managing the team to meet company objectives. 19:15 Barney: When we’re talking about the business processes I mentioned earlier, each of them are performed by different job roles. For example, for the Invoice to Cash business process, we have the Billing Specialist and Billing Managers, Collection Agents, and Cash Manager. For Procure to Pay, we have the Accounts Payable Specialist and Supervisor. Asset to Retirement, there’s the Asset Accountant and Asset Accounting Manager. The Accounting Transformation business process is usually managed by any role assigned SLA privileges. And finally, for Budget to Reporting, we have the General Accountant and General Accounting Manager. 19:55 Nikita: Thank you so much, Barney, for giving us this thorough introduction to Oracle ERP, and particularly Oracle Financials Cloud. Barney: Thanks, Niki. It was a pleasure to be here. 20:05 Lois: We’re looking forward to having you back on the podcast next week to talk about the first of the five main Financials business processes, the Invoice to Cash process flow. And to learn more about these business processes and get certified, visit mylearn.oracle.com. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 20:27 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. 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