Machine Learning Guide
Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.
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MLG 035 Large Language Models 2
05/08/2025
MLG 035 Large Language Models 2
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction. Links Notes and resources at Build the future of multi-agent software with stay healthy & sharp while you learn & code In-Context Learning (ICL) Definition: LLMs can perform tasks by learning from examples provided directly in the prompt without updating their parameters. Types: Zero-shot: Direct query, no examples provided. One-shot: Single example provided. Few-shot: Multiple examples, balancing quantity with context window limitations. Mechanism: ICL works through analogy and Bayesian inference, using examples as semantic priors to activate relevant internal representations. Emergent Properties: ICL is an "inference-time training" approach, leveraging the model’s pre-trained knowledge without gradient updates; its effectiveness can be enhanced with diverse, non-redundant examples. Retrieval Augmented Generation (RAG) and Grounding Grounding: Connecting LLMs with external knowledge bases to supplement or update static training data. Motivation: LLMs’ training data becomes outdated or lacks proprietary/specialized knowledge. Benefit: Reduces hallucinations and improves factual accuracy by incorporating current or domain-specific information. RAG Workflow: Embedding: Documents are converted into vector embeddings (using sentence transformers or representation models). Storage: Vectors are stored in a vector database (e.g., FAISS, ChromaDB, Qdrant). Retrieval: When a query is made, relevant chunks are extracted based on similarity, possibly with re-ranking or additional query processing. Augmentation: Retrieved chunks are added to the prompt to provide up-to-date context for generation. Generation: The LLM generates responses informed by the augmented context. Advanced RAG: Includes agentic approaches—self-correction, aggregation, or multi-agent contribution to source ingestion, and can integrate external document sources (e.g., web search for real-time info, or custom datasets for private knowledge). LLM Agents Overview: Agents extend LLMs by providing goal-oriented, iterative problem-solving through interaction, memory, planning, and tool usage. Key Components: Reasoning Engine (LLM Core): Interprets goals, states, and makes decisions. Planning Module: Breaks down complex tasks using strategies such as Chain of Thought or ReAct; can incorporate reflection and adjustment. Memory: Short-term via context window; long-term via persistent storage like RAG-integrated databases or special memory systems. Tools and APIs: Agents select and use external functions—file manipulation, browser control, code execution, database queries, or invoking smaller/fine-tuned models. Capabilities: Support self-evaluation, correction, and multi-step planning; allow integration with other agents (multi-agent systems); face limitations in memory continuity, adaptivity, and controllability. Current Trends: Research and development are shifting toward these agentic paradigms as LLM core scaling saturates. Multimodal Large Language Models (MLLMs) Definition: Models capable of ingesting and generating across different modalities (text, image, audio, video). Architecture: Modality-Specific Encoders: Convert raw modalities (text, image, audio) into numeric embeddings (e.g., vision transformers for images). Fusion/Alignment Layer: Embeddings from different modalities are projected into a shared space, often via cross-attention or concatenation, allowing the model to jointly reason about their content. Unified Transformer Backbone: Processes fused embeddings to allow cross-modal reasoning and generates outputs in the required format. Recent Advances: Unified architectures (e.g., GPT-4o) use a single model for all modalities rather than switching between separate sub-models. Functionality: Enables actions such as image analysis via text prompts, visual Q&A, and integrated speech recognition/generation. Advanced LLM Architectures and Training Directions Predictive Abstract Representation: Incorporating latent concept prediction alongside token prediction (e.g., via autoencoders). Patch-Level Training: Predicting larger “patches” of tokens to reduce sequence lengths and computation. Concept-Centric Modeling: Moving from next-token prediction to predicting sequences of semantic concepts (e.g., Meta’s Large Concept Model). Multi-Token Prediction: Training models to predict multiple future tokens for broader context capture. Evaluation Benchmarks (as of 2025) Key Benchmarks Used for LLM Evaluation: GPQA (Diamond): Graduate-level STEM reasoning. SWE Bench Verified: Real-world software engineering, verifying agentic code abilities. MMMU: Multimodal, college-level cross-disciplinary reasoning. HumanEval: Python coding correctness. HLE (Human’s Last Exam): Extremely challenging, multimodal knowledge assessment. LiveCodeBench: Coding with contamination-free, up-to-date problems. MLPerf Inference v5.0 Long Context: Throughput/latency for processing long contexts. MultiChallenge Conversational AI: Multiturn dialogue, in-context reasoning. TAUBench/PFCL: Tool utilization in agentic tasks. TruthfulnessQA: Measures tendency toward factual accuracy/robustness against misinformation. Prompt Engineering: High-Impact Techniques Foundational Approaches: Few-Shot Prompting: Provide pairs of inputs and desired outputs to steer the LLM. Chain of Thought: Instructing the LLM to think step-by-step, either explicitly or through internal self-reprompting, enhances reasoning and output quality. Clarity and Structure: Use clear, detailed, and structured instructions—task definition, context, constraints, output format, use of delimiters or markdown structuring. Affirmative Directives: Phrase instructions positively (“write a concise summary” instead of “don’t write a long summary”). Iterative Self-Refinement: Prompt the LLM to review and improve its prior response for better completeness, clarity, and factuality. System Prompt/Role Assignment: Assign a persona or role to the LLM for tailored behavior (e.g., “You are an expert Python programmer”). Guideline: Regularly consult official prompting guides from model developers as model capabilities evolve. Trends and Research Outlook Inference-time compute is increasingly important for pushing the boundaries of LLM task performance. Agentic LLMs and multimodal reasoning represent the primary frontiers for innovation. Prompt engineering and benchmarking remain essential for extracting optimal performance and assessing progress. Models are expected to continue evolving with research into new architectures, memory systems, and integration techniques.
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MLG 034 Large Language Models 1
05/07/2025
MLG 034 Large Language Models 1
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex task performance. Links Notes and resources at Build the future of multi-agent software with stay healthy & sharp while you learn & code Transformer Foundations and Scaling Laws Transformers: Introduced by the 2017 "Attention is All You Need" paper, transformers allow for parallel training and inference of sequences using self-attention, in contrast to the sequential nature of RNNs. Scaling Laws: Empirical research revealed that LLM performance improves predictably as model size (parameters), data size (training tokens), and compute are increased together, with diminishing returns if only one variable is scaled disproportionately. The "Chinchilla scaling law" (DeepMind, 2022) established the optimal model/data/compute ratio for efficient model performance: earlier large models like GPT-3 were undertrained relative to their size, whereas right-sized models with more training data (e.g., Chinchilla, LLaMA series) proved more compute and inference efficient. Emergent Abilities in LLMs Emergence: When trained beyond a certain scale, LLMs display abilities not present in smaller models, including: In-Context Learning (ICL): Performing new tasks based solely on prompt examples at inference time. Instruction Following: Executing natural language tasks not seen during training. Multi-Step Reasoning & Chain of Thought (CoT): Solving arithmetic, logic, or symbolic reasoning by generating intermediate reasoning steps. Discontinuity & Debate: These abilities appear abruptly in larger models, though recent research suggests that this could result from non-linearities in evaluation metrics rather than innate model properties. Architectural Evolutions: Mixture of Experts (MoE) MoE Layers: Modern LLMs often replace standard feed-forward layers with MoE structures. Composed of many independent "expert" networks specializing in different subdomains or latent structures. A gating network routes tokens to the most relevant experts per input, activating only a subset of parameters—this is called "sparse activation." Enables much larger overall models without proportional increases in compute per inference, but requires the entire model in memory and introduces new challenges like load balancing and communication overhead. Specialization & Efficiency: Experts learn different data/knowledge types, boosting model specialization and throughput, though care is needed to avoid overfitting and underutilization of specialists. The Three-Phase Training Process 1. Unsupervised Pre-Training: Next-token prediction on massive datasets—builds a foundation model capturing general language patterns. 2. Supervised Fine Tuning (SFT): Training on labeled prompt-response pairs to teach the model how to perform specific tasks (e.g., question answering, summarization, code generation). Overfitting and "catastrophic forgetting" are risks if not carefully managed. 3. Reinforcement Learning from Human Feedback (RLHF): Collects human preference data by generating multiple responses to prompts and then having annotators rank them. Builds a reward model (often PPO) based on these rankings, then updates the LLM to maximize alignment with human preferences (helpfulness, harmlessness, truthfulness). Introduces complexity and risk of reward hacking (specification gaming), where the model may exploit the reward system in unanticipated ways. Advanced Reasoning Techniques Prompt Engineering: The art/science of crafting prompts that elicit better model responses, shown to dramatically affect model output quality. Chain of Thought (CoT) Prompting: Guides models to elaborate step-by-step reasoning before arriving at final answers—demonstrably improves results on complex tasks. Variants include zero-shot CoT ("let's think step by step"), few-shot CoT with worked examples, self-consistency (voting among multiple reasoning chains), and Tree of Thought (explores multiple reasoning branches in parallel). Automated Reasoning Optimization: Frontier models selectively apply these advanced reasoning techniques, balancing compute costs with gains in accuracy and transparency. Optimization for Training and Inference Tradeoffs: The optimal balance between model size, data, and compute is determined not only for pretraining but also for inference efficiency, as lifetime inference costs may exceed initial training costs. Current Trends: Efficient scaling, model specialization (MoE), careful fine-tuning, RLHF alignment, and automated reasoning techniques define state-of-the-art LLM development.
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MLA 024 Code AI MCP Servers, ML Engineering
04/13/2025
MLA 024 Code AI MCP Servers, ML Engineering
Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments. Links Notes and resources at stay healthy & sharp while you learn & code Tool Use in Code AI Agents Code AI agents offer two primary modes of interaction: in-line code completion within the editor and agent interaction through sidebar prompts. Inline code completion has evolved from single-line suggestions to cross-file edits, refactoring, and modification of existing code blocks. Tools accessible via agents include read, write, and list file functions, as well as browser automation and command execution; permissions for sensitive actions can be set by developers. Agents can intelligently search a project’s codebase and dependencies using search commands and regular expressions to locate relevant files. Model Context Protocol (MCP) MCP, introduced by Anthropic, establishes a standardized protocol for agents to communicate with tools and services, replacing bespoke tool integrations. The protocol is analogous to REST for web servers and unifies tool calling for both local and cloud-hosted automation. MCP architecture involves three components: the AI agent, MCP client, and MCP server. The agent provides context, the client translates requests and responses, and the server executes and responds with data in a structured format. MCP servers can be local (STDIO-based for local tasks like file search or browser actions) or cloud-based (SSE for hosted APIs and SaaS tools). Developers can connect code AI agents to directories of MCP servers, accessing an expanding ecosystem of automation tools for both programming and non-programming tasks. MCP Application Examples Local MCP servers include Playwright for browser automation and Postgres MCP for live database schema analysis and data-driven UI suggestions. Cloud-based MCP servers integrate APIs such as AWS, enabling infrastructure management directly from coding environments. MCP servers are not limited to code automation; they are widely used for pipeline automation in sales, marketing, and other internet-connected workflows. Retrieval Augmented Generation (RAG) as an MCP Use Case RAG, once standard in code AI tools, indexed codebases using embeddings to assist with relevant file retrieval, but many agents now favor literal search for practicality. Local RAG MCP servers, such as Chroma or LlamaIndex, can index entire documentation sets to update agent knowledge of recent or project-specific libraries outside of widely-known frameworks. Fine-tuning a local LLM with the same documentation is an alternative approach to integrating new knowledge into code AI workflows. Machine Learning Applications Code AI tooling supports feature engineering, data cleansing, pipeline setup, model design, and hyperparameter optimization, based on real dataset distributions and project specifications. Agents can recommend advanced data transformations—such as Yeo-Johnson power transformation for skewed features—by directly analyzing example dataset distributions. Infrastructure-as-code integration enables rapid deployment of machine learning models and supporting components by chaining coding agents to cloud automation tools. Automation concepts from code AI apply to both traditional code file workflows and Jupyter Notebooks, though integration with notebooks remains less seamless. An iterative approach using sidecar Python files combined with custom instructions helps agents access necessary background and context for ML projects. Workflow Strategies for Machine Learning Engineers To leverage code AI agents in machine learning tasks, engineers can provide data samples and visualizations to agents through Python files or prompt contexts. Agents can guide creation and comparison of multiple model architectures, metrics, and loss functions, improving efficiency and broadening solution exploration. While Jupyter Lab plugin integration is currently limited, some success can be achieved by working with notebook files via code AI tools in standard code editors or by moving between notebooks and Python files for maximum flexibility.
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MLA 023 Code AI Models & Modes
04/13/2025
MLA 023 Code AI Models & Modes
Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes. Links Notes and resources at stay healthy & sharp while you learn & code Model Current Leaders According to the (as of April 12, 2025), leading models include for vibe-coding: Gemini 2.5 Pro Preview 03-25: most accurate and cost-effective option currently. Claude 3.7 Sonnet: Performs well in both architect and code modes with enabled reasoning flags. DeepSeek R1 with Claude 3.5 Sonnet: A popular combination for its balance of cost and performance between reasoning and non-reasoning tasks. Local Models Tools for Local Models: is the standard tool to manage local models, enabling usage without internet connectivity. Best Models per VRAM: See , but know that Qwen 3 launched after that; and DeepSeek R1 is coming soon. Privacy and Security: Utilizing local models enhances data security, suitable for sensitive projects or corporate environments that require data to remain onsite. Performance Trade-offs: Local models, due to distillation and size constraints, often perform slightly worse than cloud-hosted models but offer privacy benefits. Fine-Tuning Models Customization: Developers can fine-tune pre-trained models to specialize them for their specific codebase, enhancing relevance and accuracy. Advanced Usage: Suitable for long-term projects, fine-tuning helps models understand unique aspects of a project, resulting in consistent code quality improvements. Tips and Best Practices Judicious Use of the @ Key: Improves model efficiency by specifying the context of commands, reducing the necessity for AI-initiated searches. Examples include specifying file paths, URLs, or git commits to inform AI actions more precisely. Concurrent Feature Implementation: Leverage tools like to manage multiple features simultaneously, acting more as a manager overseeing several tasks at once, enhancing productivity. Continued Learning: Staying updated with documentation, particularly 's, due to its comprehensive feature set and versatility among AI coding tools.
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MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf
02/09/2025
MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf
Vibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows. Links Notes and resources at stay healthy & sharp while you learn & code Definition and Context of Vibe Coding Vibe coding refers to using large language models (LLMs) to generate or edit code directly within IDEs or through plugins. Developers interface with AI models in their coding environment by entering requests or commands in chat-like dialogues, enabling streamlined workflows for feature additions, debugging, and other tasks. Industry Reception and Concerns Industry skepticism about vibe coding centers on three issues: concerns that excessive reliance on AI can degrade code quality, skepticism over aggressive marketing reminiscent of early cryptocurrency promotions, and anxieties about job security among experienced developers. Maintaining human oversight and reviewing AI-generated changes is emphasized, with both senior engineers and newcomers encouraged to engage critically with outputs rather than use them blindly. Turnkey Web App Generators vs. Developer-Focused Tools Some AI-powered platforms function as turnkey website and app generators (for example, Lovable, Rept, and Bolt), which reduce development to prompting but limit customizability and resemble content management systems. The focus of this episode is on developer-oriented tools that operate within professional environments, distinguishing them from these all-in-one generators. Evolution of Code AI Tools and IDE Integration Most contemporary AI code assistants either fork Visual Studio Code (, ), or offer plugins/extensions for it, capitalizing on the popularity and adaptability of VS Code. Tools such as , , , and present varied approaches ranging from command-line interfaces to customizable, open-source integrations. Functional Capabilities: Inline Edits and Agentic Features Early iterations of AI coding tools mainly provided inline code suggestions or autocompletions within active files. Modern tools now offer “agentic” features, such as analyzing file dependencies, editing across multiple files, installing packages, executing commands, interacting with web browsers, and performing broader codebase actions. Detailed Overview of Leading Tools is a popular standalone fork of VS Code, focused on integrating new models with stability and offering a flat-fee pricing model. offers similar agentic and inline features with tiered pricing and a “just works” usability orientation. , integrated with VS Code and GitHub Code Spaces, provides agentic coding with periodic performance fluctuations and tiered pricing. is open-source and model-agnostic, pioneering features like “bring your own model” (BYOM) and operating on a per-request billing structure. , derived from Cline, prioritizes rapid feature development and customization, serving users interested in experimental capabilities. is command-line only, focusing on token efficiency and precise, targeted code modifications, making it useful for surgical edits or as a fallback tool. Community and Resource Ecosystem Resources such as enable developers to monitor progress and compare tool effectiveness. Aiding community support and updates, the discusses use cases, troubleshooting, and rapid feature rollouts. Demonstrations such as the illustrate tool capabilities in practical scenarios. Models, Pricing, and Cost Management Subscription tools like Cursor, Copilot, and Windsurf have flat or tiered pricing, with extra fees for exceeding standard quotas. Open-source solutions require API keys for model providers (OpenAI, Anthropic, Google Gemini), incurring per-request charges dependent on usage. is recommended for consolidating credits and accessing multiple AI models, streamlining administration and reducing fragmented expenses. Model Advancements and Recommendations The landscape of model performance changes rapidly, with leaders shifting from Claude 3.5, to DeepSeek, Claude 3.7, and currently to Gemini 2.5 Pro Experimental, which is temporarily free and offers extended capabilities. Developers should periodically review available models, utilizing OpenRouter to select up-to-date and efficient options. Practical Usage Strategies For routine development, begin with Cursor and explore alternatives like Copilot and Windsurf for additional features. Advanced users can install or as plugins within preferred IDEs, and maintain for precise code changes or fallback needs. Balancing subscription-based and open-source tools can increase cost-efficiency; thoughtful review of AI-generated edits remains essential before codebase integration. Conclusion Vibe coding, defined as using LLMs for software and machine learning development, is transforming professional workflows with new tooling and shifting best practices. Developers are encouraged to experiment with a range of tools, monitor ongoing advancements, and integrate AI responsibly into their coding routines.
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MLG 033 Transformers
02/09/2025
MLG 033 Transformers
Links: Notes and resources at 3Blue1Brown videos: stay healthy & sharp while you learn & code audio/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization. Positional Encodings: Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order. Self-Attention Mechanism Q, K, V Explained: Query (Q): The representation of the token seeking contextual info. Key (K): The representation of tokens being compared against. Value (V): The information to be aggregated based on the attention scores. Multi-Head Attention: Splits Q, K, V into multiple “heads” to capture diverse relationships and nuances across different subspaces. Dot-Product & Scaling: Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly. Masking Causal Masking: In autoregressive models, prevents a token from “seeing” future tokens, ensuring proper generation. Padding Masks: Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions. Feed-Forward Networks (MLPs) Transformation & Storage: Post-attention MLPs apply non-linear transformations; many argue they’re where the “facts” or learned knowledge really get stored. Depth & Expressivity: Their layered nature deepens the model’s capacity to represent complex patterns. Residual Connections & Normalization Residual Links: Crucial for gradient flow in deep architectures, preventing vanishing/exploding gradients. Layer Normalization: Stabilizes training by normalizing across features, enhancing convergence. Scalability & Efficiency Considerations Parallelization Advantage: Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs. Complexity Trade-offs: Self-attention’s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention. Training Paradigms & Emergent Properties Pretraining & Fine-Tuning: Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm. Emergent Behavior: With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked. Interpretability & Knowledge Distribution Distributed Representation: “Facts” aren’t stored in a single layer but are embedded throughout both attention heads and MLP layers. Debate on Attention: While some see attention weights as interpretable, a growing view is that real “knowledge” is diffused across the network’s parameters.
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MLA 021 Databricks: Cloud Analytics and MLOps
06/22/2022
MLA 021 Databricks: Cloud Analytics and MLOps
Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at stay healthy & sharp while you learn & code Raybeam and Databricks Raybeam is a data science and analytics company, recently acquired by Dept Agency. While Raybeam focuses on data analytics, its acquisition has expanded its expertise into ML Ops and AI. The company recommends tools based on client requirements, frequently utilizing Databricks for its comprehensive nature. Understanding Databricks Databricks is not merely an analytics platform; it is a competitor in the ML Ops space alongside tools like SageMaker and Kubeflow. It provides interactive notebooks, Python code execution, and runs on a hosted Apache Spark cluster. Databricks includes Delta Lake, which acts as a storage and data management layer. Choosing the Right MLOps Tool Raybeam evaluates each client’s needs, existing expertise, and infrastructure before recommending a platform. Databricks, SageMaker, Kubeflow, and Snowflake are common alternatives, with the final selection dependent on current pipelines and operational challenges. Maintaining existing workflows is prioritized unless scalability or feature limitations necessitate migration. Databricks Features Databricks is accessible via a web interface similar to Jupyter Hub and can be integrated with local IDEs (e.g., VS Code, PyCharm) using Databricks Connect. Notebooks on Databricks can be version-controlled with Git repositories, enhancing collaboration and preventing data loss. The platform supports configuration of computing resources to match model size and complexity. Databricks clusters are hosted on AWS, Azure, or GCP, with users selecting the underlying cloud provider at sign-up. Parquet and Delta Lake Parquet files store data in a columnar format, which improves efficiency for aggregation and analytics tasks. Delta Lake provides transactional operations on top of Parquet files by maintaining a version history, enabling row edits and deletions. This approach offers a database-like experience for handling large datasets, simplifying both analytics and machine learning workflows. Pricing and Usage Pricing for Databricks depends on the chosen cloud provider (AWS, Azure, or GCP) with an additional fee for Databricks’ services. The added cost is described as relatively small, and the platform is accessible to both individual developers and large enterprises. Databricks is recommended for newcomers to data science and ML for its breadth of features and straightforward setup. Databricks, MLflow, and Other Integrations Databricks provides a hosted MLflow solution, offering experiment tracking and model management. The platform can access data stored in services like S3, Snowflake, and other cloud provider storage options. Integration with tools such as PyArrow is supported, facilitating efficient data access and manipulation. Example Use Cases and Decision Process Migration to Databricks is recommended when a client’s existing infrastructure (e.g., on-premises Spark clusters) cannot scale effectively. The selection process involves an in-depth exploration of a client’s operational challenges and goals. Databricks is chosen for clients lacking feature-specific needs but requiring a unified data analytics and ML platform. Personal Projects by Ming Chang Ming Chang has explored automated stock trading using APIs such as Alpaca, focusing on downloading and analyzing market data. He has also developed drone-related projects with Raspberry Pi, emphasizing real-world applications of programming and physical computing. Additional Resources
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MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
01/29/2022
MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at stay healthy & sharp while you learn & code - Data Scientist at Dept Agency Managed vs. Open-Source ML Pipeline Orchestration Cloud providers such as AWS, Google Cloud, and Azure offer managed machine learning orchestration solutions, including SageMaker (AWS) and Vertex AI (GCP). Managed services provide integrated environments that are easier to set up and operate but often result in vendor lock-in, limiting portability across cloud platforms. Open-source tools like Kubeflow extend Kubernetes to support end-to-end machine learning pipelines, enabling portability across AWS, GCP, Azure, or on-premises environments. Introduction to Kubeflow is an open-source project aimed at making machine learning workflow deployment on Kubernetes simple, portable, and scalable. Kubeflow enables data scientists and ML engineers to build, orchestrate, and monitor pipelines using popular frameworks such as TensorFlow, scikit-learn, and PyTorch. Kubeflow can integrate with for complete end-to-end ML pipelines, covering data ingestion, preprocessing, model training, evaluation, and deployment. Machine Learning Pipelines: Concepts and Motivation Production machine learning systems involve not just model training but also complex pipelines for data ingestion, feature engineering, validation, retraining, and monitoring. Pipelines automate retraining based on model performance drift or updated data, supporting continuous improvement and adaptation to changing data patterns. Scalable, orchestrated pipelines reduce manual overhead, improve reproducibility, and ensure that models remain accurate as underlying business conditions evolve. Pipeline Orchestration Analogies and Advantages ML pipeline orchestration tools in machine learning fulfill a role similar to continuous integration and continuous deployment (CI/CD) in traditional software engineering. Pipelines enable automated retraining, modularization of pipeline steps (such as ingestion, feature transformation, and deployment), and robust monitoring. Adopting pipeline orchestrators, rather than maintaining standalone models, helps organizations handle multiple models and varied business use cases efficiently. Choosing Between Managed and Open-Source Solutions Managed services (e.g., SageMaker, Vertex AI) offer streamlined user experiences and seamless integration but restrict cross-cloud flexibility. Kubeflow, as an open-source platform on Kubernetes, enables cross-platform deployment, integration with multiple ML frameworks, and minimizes dependency on a single cloud provider. The complexity of Kubernetes and Kubeflow setup is offset by significant flexibility and community-driven improvements. Cross-Cloud and Local Development Kubeflow operates on any Kubernetes environment including AWS EKS, GCP GKE, and Azure AKS, as well as on-premises or local clusters. Local and cross-cloud development are facilitated in Kubeflow, while managed services like SageMaker and Vertex AI are better suited to cloud-native workflows. Debugging and development workflows can be challenging in highly secured cloud environments; Kubeflow’s local deployment flexibility addresses these hurdles. Relationship to TensorFlow Extended (TFX) and Machine Learning Frameworks TensorFlow Extended (TFX) is an end-to-end platform for creating production ML pipelines, tightly integrated with Kubeflow for deployment and execution. While Kubeflow originally focused on TensorFlow, it has grown to support PyTorch, scikit-learn, and other major ML frameworks, offering wider applicability. TFX provides modular pipeline components (data ingestion, transformation, validation, model training, evaluation, and deployment) that execute within Kubeflow’s orchestration platform. Alternative Pipeline Orchestration Tools is a general-purpose workflow orchestrator using DAGs, suited for data engineering and automation, but less resource-capable for heavy ML training within the pipeline. Airflow often submits jobs to external compute resources (e.g., AI Platform) for resource-intensive workloads. In organizations using both Kubeflow and Airflow, Airflow may handle data workflows, while Kubeflow is reserved for ML pipelines. and other solutions also exist, each with unique integrations and strengths; their adoption depends on use case requirements. Selecting a Cloud Platform and Orchestration Approach The optimal choice of cloud platform and orchestration tool is typically guided by client needs, existing integrations (e.g., organizational use of Google or Microsoft solutions), and team expertise. Agencies with diverse client portfolios often benefit from open-source, cross-cloud tools like Kubeflow to maximize flexibility and knowledge sharing across projects. Users entrenched in a single cloud provider may prefer managed offerings for ease of use and integration, while those prioritizing portability and flexibility often choose open-source solutions. Cost Optimization in Model Training Both AWS and GCP offer cost-saving compute options for training, such as spot instances (AWS) and preemptible instances (GCP), which are suitable for non-production, batch training jobs. Production workloads that require high uptime and reliability do not typically utilize cost-saving transient compute resources, as these can be interrupted. Machine Learning Project Lifecycle Overview Project initiation begins with data discovery and validation of the client’s requirements against available data. Cloud environment selection is influenced by client infrastructure, business applications, and platform integrations rather than solely by technical features. Data cleaning, exploratory analysis, model prototyping, advanced model refinement, and deployment are handled collaboratively with data engineering and machine learning teams. The pipeline is gradually constructed in modular steps, facilitating scalable, automated retraining and integration with business applications. Educational Pathways for Data Science and Machine Learning Careers Advanced mathematics or statistics education provides a strong foundation for work in data science and machine learning. Master’s degrees in data science add the most value for candidates from non-technical undergraduate backgrounds; those with backgrounds in statistics, mathematics, or computer science may benefit more from self-study or targeted upskilling. When evaluating online or accelerated degree programs, candidates should scrutinize the curriculum, instructor engagement, and peer interaction to ensure comprehensive learning.
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MLA 019 Cloud, DevOps & Architecture
01/13/2022
MLA 019 Cloud, DevOps & Architecture
The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at stay healthy & sharp while you learn & code ;## Translating Machine Learning Models to Production After developing and training a machine learning model locally or using cloud tools like AWS SageMaker, it must be deployed to reach end users. A typical deployment stack involves the trained model exposed via a SageMaker endpoint, a backend server (e.g., Python FastAPI on AWS ECS with Fargate), a managed database (such as AWS RDS Postgres), an application load balancer (ALB), and a public-facing frontend (e.g., React app hosted on S3 with CloudFront and Route 53). Infrastructure as Code and Automation Tools Infrastructure as code (IaC) manages deployment and maintenance of cloud resources using tools like Terraform, allowing environments to be version-controlled and reproducible. Terraform is favored for its structured approach and cross-cloud compatibility, while other tools like Cloud Formation (AWS-specific) and Pulumi offer alternative paradigms. Configuration management tools such as Ansible, Chef, and Puppet automate setup and software installation on compute instances but are increasingly replaced by containerization and Dockerfiles. Continuous Integration and Continuous Deployment (CI/CD) pipelines (with tools like AWS CodePipeline or CircleCI) automate builds, testing, and code deployment to infrastructure. Containers, Orchestration, and Cloud Choices Containers, enabled by Docker, allow developers to encapsulate applications and dependencies, facilitating consistency across environments from local development to production. Deployment options include AWS ECS/Fargate for managed orchestration, Kubernetes for large-scale or multi-cloud scenarios, and simpler services like AWS App Runner and Elastic Beanstalk for small-scale applications. Kubernetes provides robust flexibility and cross-provider support but brings high complexity, making it best suited for organizations with substantial infrastructure needs and experienced staff. Use of cloud services versus open-source alternatives on Kubernetes (e.g., RDS vs. Postgres containers) affects manageability, vendor lock-in, and required expertise. DevOps and Architecture: Roles and Collaboration DevOps unites development and operations through common processes and tooling to accelerate safe production deployments and improve coordination. Architecture focuses on the holistic design of systems, establishing how different technical components fit together and serve overall business or product goals. There is significant overlap, but architecture plans and outlines systems, while DevOps engineers implement, automate, and monitor deployment and operations. Cross-functional collaboration is essential, as machine learning engineers, DevOps, and architects must communicate requirements, constraints, and changes, especially regarding production-readiness and security. Security, Scale, and When to Seek Help Security is a primary concern when moving to production, especially if handling sensitive data or personally identifiable information (PII); professional DevOps involvement is strongly advised in such cases. Common cloud security pitfalls include publicly accessible networks, insecure S3 buckets, and improper handling of secrets and credentials. For experimentation or small-scale safe projects, machine learning engineers can use tools like Terraform, Docker, and AWS managed services, but should employ cloud cost monitoring to avoid unexpected bills. Cloud Providers and Service Considerations AWS dominates the cloud market, followed by Azure (strong in enterprise/Microsoft-integrated environments) and Google Cloud Platform (GCP), which offers a strong user interface but has a record of sunsetting products. Managed cloud machine learning services, such as AWS SageMaker and GCP Vertex AI, streamline model training, deployment, and monitoring. Vendor-specific tools simplify management but limit portability, while Kubernetes and its ML pipelines (e.g., Kubeflow, Apache Airflow) provide open-source, cross-cloud options with greater complexity. Recommended Learning Paths and Community Resources Learning and prototyping with Terraform, Docker, and basic cloud services is encouraged to understand deployment pipelines, but professional security review is critical before handling production-sensitive data. For those entering DevOps, structured learning with platforms like aCloudGuru or AWS’s own curricula can provide certification-ready paths. Continual learning is necessary, as tooling and best practices evolve rapidly. Reference Links Expert coworkers at Dept - Principal Software Developer - DevOps Lead (frequent discussions on DevOps and architecture) DevOps Tools Visual Guides and Comparisons Learning Resources
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MLA 017 AWS Local Development Environment
11/06/2021
MLA 017 AWS Local Development Environment
AWS development environments for local and cloud deployment can differ significantly, leading to extra complexity and setup during cloud migration. By developing directly within AWS environments, using tools such as Lambda, Cloud9, SageMaker Studio, client VPN connections, or LocalStack, developers can streamline transitions to production and leverage AWS-managed services from the start. This episode outlines three primary strategies for treating AWS as your development environment, details the benefits and tradeoffs of each, and explains the role of infrastructure-as-code tools such as Terraform and CDK in maintaining replicable, trackable cloud infrastructure. Links Notes and resources at stay healthy & sharp while you learn & code Docker Fundamentals for Development Docker containers encapsulate operating systems, packages, and code, which simplifies dependency management and deployment. Files are added to containers using either the COPY command for one-time inclusion during a build or the volume directive for live synchronization during development. Docker Compose orchestrates multiple containers on a local environment, while Kubernetes is used at larger scale for container orchestration in the cloud. Docker and AWS Integration Docker is frequently used in AWS, including for packaging and deploying Lambda functions, SageMaker jobs, and ECS/Fargate containers. Deploying complex applications like web servers and databases on AWS involves using services such as ECR for image storage, ECS/Fargate for container management, RDS for databases, and requires configuration of networking components such as VPCs, subnets, and security groups. Challenges in Migrating from Localhost to AWS Local Docker Compose setups differ considerably from AWS managed services architecture. Migrating to AWS involves extra steps such as pushing images to ECR, establishing networking with VPCs, configuring load balancers or API Gateway, setting up domain names with Route 53, and integrating SSL certificates via ACM. Configuring internal communication between services and securing databases adds complexity compared to local development. Strategy 1: Developing Entirely in the AWS Cloud Developers can use AWS Lambda’s built-in code editor, Cloud9 IDE, and SageMaker Studio to edit, run, and deploy code directly in the AWS console. Cloud-based development is not tied to a single machine and eliminates local environment setup. While convenient, in-browser IDEs like Cloud9 and SageMaker Studio are less powerful than established local tools like PyCharm or DataGrip. Strategy 2: Local Development Connected to AWS via Client VPN The AWS Client VPN enables local machines to securely access AWS VPC resources, such as RDS databases or Lambda endpoints, as if they were on the same network. This approach allows developers to continue using their preferred local IDEs while testing code against actual cloud services. Storing sensitive credentials is handled by AWS Secrets Manager instead of local files or environment variables. Example tutorials and instructions: Strategy 3: Local Emulation of AWS Using LocalStack provides local, Docker-based emulation of AWS services, allowing development and testing without incurring cloud costs or latency. The project offers a free tier supporting core serverless services and a paid tier covering more advanced features like RDS, ACM, and Route 53. LocalStack supports mounting local source files into Lambda functions, enabling direct development on the local machine with changes immediately reflected in the emulated AWS environment. This approach brings rapid iteration and cost savings, but coverage of AWS features may vary, especially for advanced or new AWS services. Infrastructure as Code: Managing AWS Environments Managing AWS resources through the web console is not sustainable for tracking or reproducing environments. Infrastructure as code (IaC) tools such as , , and enable declarative, version-controlled description and deployment of AWS services. Terraform offers broad multi-cloud compatibility and support for both managed and cloud-native services, whereas CDK is AWS-specific and typically more streamlined but supports fewer services. Changes made via IaC tools are automatically propagated to dependent resources, reducing manual error and ensuring consistency across environments. Benefits of AWS-First Development Developing directly in AWS or with local emulation ensures alignment between development, staging, and production environments, reducing last-minute deployment issues. Early use of AWS services can reveal managed solutions—such as Cognito for authentication or Data Wrangler for feature transformation—that are more scalable and secure than homegrown implementations. Infrastructure as code provides reproducibility, easier team onboarding, and disaster recovery. Alternatives and Kubernetes Kubernetes represents a different model of orchestrating containers and services, generally leveraging open source components inside Docker containers, independent of managed AWS services. While Kubernetes can manage deployments to AWS (via EKS), GCP, or Azure, its architecture and operational concerns differ from AWS-native development patterns. Additional AWS IDEs and Services Conclusion Choosing between developing in the AWS cloud, connecting local environments via VPN, or using tools like LocalStack depends on team needs, budget, and workflow preferences. Emphasizing infrastructure as code ensures environments remain consistent, maintainable, and easily reproducible.
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MLA 016 AWS SageMaker MLOps 2
11/05/2021
MLA 016 AWS SageMaker MLOps 2
SageMaker streamlines machine learning workflows by enabling integrated model training, tuning, deployment, monitoring, and pipeline automation within the AWS ecosystem, offering scalable compute options and flexible development environments. Cloud-native AWS machine learning services such as Comprehend and Poly provide off-the-shelf solutions for NLP, time series, recommendations, and more, reducing the need for custom model implementation and deployment. Links Notes and resources at stay healthy & sharp while you learn & code Model Training and Tuning with SageMaker SageMaker enables model training within integrated data and ML pipelines, drawing from components such as Data Wrangler and Feature Store for a seamless workflow. Using SageMaker for training eliminates the need for manual transitions from local environments to the cloud, as models remain deployable within the AWS stack. SageMaker Studio offers a browser-based IDE environment with iPython notebook support, providing collaborative editing, sharing, and development without the need for complex local setup. Distributed, parallel training is supported with scalable EC2 instances, including AWS-proprietary chips for optimized model training and inference. SageMaker's Model Debugger and monitoring tools aid in tracking performance metrics, model drift, and bias, offering alerts via CloudWatch and accessible graphical interfaces. Flexible Development and Training Environments SageMaker supports various model creation approaches, including default AWS environments with pre-installed data science libraries, bring-your-own Docker containers, and hybrid customizations via requirements files. SageMaker JumpStart provides quick-start options for common ML tasks, such as computer vision or NLP, with curated pre-trained models and environment setups optimized for SageMaker hardware and operations. Users can leverage Autopilot for end-to-end model training and deployment with minimal manual configuration or start from JumpStart templates to streamline typical workflows. Hyperparameter Optimization and Experimentation SageMaker Experiments supports automated hyperparameter search and optimization, using Bayesian optimization to evaluate and select the best performing configurations. Experiments and training runs are tracked, logged, and stored for future reference, allowing efficient continuation of experimentation and reuse of successful configurations as new data is incorporated. Model Deployment and Inference Options Trained models can be deployed as scalable REST endpoints, where users specify required EC2 instance types, including inference-optimized chips. Elastic Inference allows attachment of specialized hardware to reduce costs and tailor inference environments. Batch Transform is available for non-continuous, ad-hoc, or large batch inference jobs, enabling on-demand scaling and integration with data pipelines or serverless orchestration. ML Pipelines, CI/CD, and Monitoring SageMaker Pipelines manages the orchestration of ML workflows, supporting CI/CD by triggering retraining and deployments based on code changes or new data arrivals. CI/CD automation includes not only code unit tests but also automated monitoring of metrics such as accuracy, drift, and bias thresholds to qualify models for deployment. Monitoring features (like Model Monitor) provide ongoing performance assessments, alerting stakeholders to significant changes or issues. Integrations and Deployment Flexibility SageMaker supports integration with Kubernetes via EKS, allowing teams to leverage universal orchestration for containerized ML workloads across cloud providers or hybrid environments. The SageMaker Neo service optimizes and packages trained models for deployment to edge devices, mobile hardware, and AWS Lambda, reducing runtime footprint and syncing updates as new models become available. Cloud-Native AWS ML Services AWS offers a variety of cloud-native services for common ML tasks, accessible via REST or SDK calls and managed by AWS, eliminating custom model development and operations overhead. Comprehend for document clustering, sentiment analysis, and other NLP tasks. Forecast for time series prediction. Fraud Detector for transaction monitoring. Lex for chatbot workflows. Personalize for recommendation systems. Poly for text-to-speech conversion. Textract for OCR and data extraction from complex documents. Translate for machine translation. Panorama for computer vision on edge devices. These services continuously improve as AWS retrains and updates their underlying models, transferring benefits directly to customers without manual intervention. Application Example: Migrating to SageMaker and AWS Services When building features such as document clustering, question answering, or recommendations, first review whether cloud-native services like Comprehend can fulfill requirements prior to investing in custom ML models. For custom NLP tasks not available in AWS services, use SageMaker to manage model deployment (e.g., deploying pre-trained Hugging Face Transformers for summarization or embeddings). Batch inference and feature extraction jobs can be triggered using SageMaker automation and event notifications, supporting modular, scalable, and microservices-friendly architectures. Tabular prediction and feature importance can be handled by pipe-lining data from relational stores through SageMaker Autopilot or traditional algorithms such as XGBoost. Recommendation workflows can combine embeddings, neural networks, and event triggers, with SageMaker handling monitoring, scaling, and retraining in response to user feedback and data drift. General Usage Guidance and Strategy Employ AWS cloud-native services where possible to minimize infrastructure management and accelerate feature delivery. Use SageMaker JumpStart and Autopilot to jump ahead in common ML scenarios, falling back to custom code and containers only when unique use cases demand. Leverage SageMaker tools for pipeline orchestration, monitoring, retraining, and model deployment to ensure scalable, maintainable, and up-to-date ML workflows. Useful Links
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MLA 015 AWS SageMaker MLOps 1
11/04/2021
MLA 015 AWS SageMaker MLOps 1
SageMaker is an end-to-end machine learning platform on AWS that covers every stage of the ML lifecycle, including data ingestion, preparation, training, deployment, monitoring, and bias detection. The platform offers integrated tools such as Data Wrangler, Feature Store, Ground Truth, Clarify, Autopilot, and distributed training to enable scalable, automated, and accessible machine learning operations for both tabular and large data sets. Links Notes and resources at stay healthy & sharp while you learn & code Amazon SageMaker: The Machine Learning Operations Platform MLOps is deploying your ML models to the cloud. See for an overview of tooling (also generally a great ML educational run-down.) Introduction to SageMaker and MLOps SageMaker is a comprehensive platform offered by AWS for machine learning operations (MLOps), allowing full lifecycle management of machine learning models. Its popularity provides access to extensive resources, educational materials, community support, and job market presence, amplifying adoption and feature availability. SageMaker can replace traditional local development environments, such as setups using Docker, by moving data processing and model training to the cloud. Data Preparation in SageMaker SageMaker manages diverse data ingestion sources such as CSV, TSV, Parquet files, databases like RDS, and large-scale streaming data via AWS Kinesis Firehose. The platform introduces the concept of data lakes, which aggregate multiple related data sources for big data workloads. Data Wrangler is the entry point for data preparation, enabling ingestion, feature engineering, imputation of missing values, categorical encoding, and principal component analysis, all within an interactive graphical user interface. Data wrangler leverages distributed computing frameworks like Apache Spark to process large volumes of data efficiently. Visualization tools are integrated for exploratory data analysis, offering table-based and graphical insights typically found in specialized tools such as Tableau. Feature Store Feature Store acts as a centralized repository to save and manage transformed features created during data preprocessing, ensuring different steps in the pipeline access consistent, reusable feature sets. It facilitates collaboration by making preprocessed features available to various members of a data science team and across different models. Ground Truth: Data Labeling Ground Truth provides automated and manual data labeling options, including outsourcing to Amazon Mechanical Turk or assigning tasks to internal employees via a secure AWS GUI. The system ensures quality by averaging multiple annotators’ labels and upweighting reliable workers, and can also perform automated label inference when partial labels exist. This flexibility addresses both sensitive and high-volume labeling requirements. Clarify: Bias Detection Clarify identifies and analyzes bias in both datasets and trained models, offering measurement and reporting tools to improve fairness and compliance. It integrates seamlessly with other SageMaker components for continuous monitoring and re-calibration in production deployments. Build Phase: Model Training and AutoML SageMaker Studio offers a web-based integrated development environment to manage all aspects of the pipeline visually. Autopilot automates the selection, training, and hyperparameter optimization of machine learning models for tabular data, producing an optimal model and optionally creating reproducible code notebooks. Users can take over the automated pipeline at any stage to customize or extend the process if needed. Debugger and Distributed Training Debugger provides real-time training monitoring, similar to TensorBoard, and offers notifications for anomalies such as vanishing or exploding gradients by integrating with AWS CloudWatch. SageMaker’s distributed training feature enables users to train models across multiple compute instances, optimizing for hardware utilization, cost, and training speed. The system allows for sharding of data and auto-scaling based on resource utilization monitored via CloudWatch notifications. Summary Workflow and Scalability The SageMaker pipeline covers every aspect of machine learning workflows, from ingestion, cleaning, and feature engineering, to training, deployment, bias monitoring, and distributed computation. Each tool is integrated to provide either no-code, low-code, or fully customizable code interfaces. The platform supports scaling from small experiments to enterprise-level big data solutions. Useful AWS and SageMaker Resources
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MLA 014 Machine Learning Hosting and Serverless Deployment
01/18/2021
MLA 014 Machine Learning Hosting and Serverless Deployment
Machine learning model deployment on the cloud is typically handled with solutions like AWS SageMaker for end-to-end training and inference as a REST endpoint, AWS Batch for cost-effective on-demand batch jobs using Docker containers, and AWS Lambda for low-usage, serverless inference without GPU support. Storage and infrastructure options such as AWS EFS are essential for managing large model artifacts, while new tools like Cortex offer open source alternatives with features like cost savings and scale-to-zero for resource management. Links Notes and resources at stay healthy & sharp while you learn & code Cloud Providers for Machine Learning Hosting The major cloud service providers for machine learning hosting are Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. AWS is widely adopted due to rapid innovation, a large ecosystem, extensive documentation, and ease of integration with other AWS services, despite some features of GCP, such as TPUs, being attractive for specific use cases. Core Machine Learning Hosting Services 1. AWS SageMaker SageMaker is an end-to-end service for training, monitoring, and deploying machine learning models, including REST endpoint deployment for inference. It features auto-scaling, built-in monitoring, and support for Jupyter notebooks, but it incurs at least a 40% cost premium over direct EC2 usage and is always-on, which can be costly for low-traffic applications. provides REST endpoint deployment and training analytics. Google Cloud offers with similar functionality. 2. AWS Batch AWS Batch allows one-off batch jobs, typically for resource-intensive ML training or infrequent inference, using Docker containers. Batch supports spot instances for significant cost savings and automatically shuts down resources when jobs complete, reducing always-on costs. Batch jobs can be triggered via CLI, console, or programmatically, and the service does not provide automatic deployment or monitoring functionality like SageMaker. enables Docker-based batch jobs and leverages for container hosting. 3. AWS Lambda AWS Lambda provides serverless deployment for machine learning inference, auto-scaling to meet demand, and incurs costs only during actual usage, but it does not support GPU or Elastic Inference. Lambda functions can utilize attached for storing and loading large model artifacts, which helps manage deployment size and cold start performance. Only models that can perform inference efficiently on CPU within Lambda’s memory and compute limits are suitable for this approach. 4. Elastic Inference and Persistent Storage AWS Elastic Inference enables the attachment of fractional GPU resources to EC2 or SageMaker for inference workloads, driving down costs by avoiding full GPU allocation. AWS EFS (Elastic File System) is used to provide persistent, shared storage for model artifacts, allowing services like Batch and Lambda to efficiently access large files without repeated downloads. allows mounting persistent file systems across services. Model Optimization and Compatibility Model optimizers such as ONNX (Open Neural Network Exchange) and Intel’s OpenVINO can compress and optimize machine learning models for efficient inference, enabling CPU-only deployment with minimal loss of accuracy. ONNX helps convert models to a format that is interoperable across different frameworks and architectures, which supports serverless environments like Lambda. Emerging and Alternative Providers 1. Cortex is an open source system that orchestrates model training, deployment, and scaling on AWS, including support for spot instances and potential for scale-to-zero, reducing costs during idle periods. Cortex aims to provide SageMaker-like capabilities without the additional premium and with greater flexibility over infrastructure management. 2. Other Providers and are additional providers offering ML model training and deployment services with cost-competitive offerings versus AWS. PaperSpace is highlighted as significantly less expensive than SageMaker and Batch, though AWS integration and ecosystem breadth may still steer users toward AWS-native solutions. Batch and Endpoint Model Deployment Scenarios If model usage is rare (e.g., 1–50 times per day), batch approaches such as AWS Batch are cost-effective, running containerized jobs as needed and then shutting down. For customer-facing applications requiring consistently available models, endpoint-based services like SageMaker, GCP Cloud ML, or Cortex are more appropriate. Orchestration and Advanced Architectures Kubernetes and related tools can be used to orchestrate ML models and complex pipelines at scale, enabling integration of components such as API gateways, serverless functions, and scalable training and inference systems. Tools like KubeFlow leverage Kubernetes for deploying machine learning workloads, but require higher expertise and greater management effort. Summary Table of Linked Services
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MLA 013 Tech Stack for Customer-Facing Machine Learning Products
01/03/2021
MLA 013 Tech Stack for Customer-Facing Machine Learning Products
Primary technology recommendations for building a customer-facing machine learning product include React and React Native for the front end, serverless platforms like AWS Amplify or GCP Firebase for authentication and basic server/database needs, and Postgres as the relational database of choice. Serverless approaches are encouraged for scalability and security, with traditional server frameworks and containerization recommended only for advanced custom backend requirements. When serverless options are inadequate, use Node.js with Express or FastAPI in Docker containers, and consider adding Redis for in-memory sessions and RabbitMQ or SQS for job queues, though many of these functions can be handled by Postgres. The machine learning server itself, including deployment strategies, will be discussed separately. Links Notes and resources at stay healthy & sharp while you learn & code Client Applications React is recommended as the primary web front-end framework due to its compositional structure, best practice enforcement, and strong community support. React Native is used for mobile applications, enabling code reuse and a unified JavaScript codebase for web, iOS, and Android clients. Using React and React Native simplifies development by allowing most UI logic to be written in a single language. Server (Backend) Options The episode encourages starting with serverless frameworks, such as AWS Amplify or GCP Firebase, for rapid scaling, built-in authentication, and security. Amplify allows seamless integration with React and handles authentication, user management, and database access directly from the client. When direct client-to-database access is insufficient, custom business logic can be implemented using AWS Lambda or Google Cloud Functions without managing entire servers. Only when serverless frameworks are insufficient should developers consider managing their own server code. Recommended traditional backend options include Node.js with Express for JavaScript environments or FastAPI for Python-centric projects, both offering strong concurrency support. Using Docker to containerize server code and deploying via managed orchestration (e.g., AWS ECS/Fargate) provides flexibility and migration capability beyond serverless. Python's FastAPI is advised for developers heavily invested in the Python ecosystem, especially if machine learning code is also in Python. Database and Supporting Infrastructure Postgres is recommended as the primary relational database, owing to its advanced features, community momentum, and versatility. Postgres can serve multiple infrastructure functions beyond storage, including job queue management and pub/sub (publish-subscribe) messaging via specific database features. NoSQL options such as MongoDB are only recommended when hierarchical, non-tabular data models or specific performance optimizations are necessary. For situations requiring in-memory session management or real-time messaging, Redis is suggested, but Postgres may suffice for many use cases. Job queuing can be accomplished with external tools like RabbitMQ or AWS SQS, but Postgres also supports job queuing via transactional locks. Cloud Hosting and Server Management Serverless deployment abstracts away infrastructure operations, improving scalability and reducing ongoing server management and security burdens. Serverless functions scale automatically and only incur charges during execution. Amplify and Firebase offer out-of-the-box user authentication, database, and cloud function support, while custom authentication can be handled with tools like AWS Cognito. Managed database hosting (e.g., AWS RDS for Postgres) simplifies backups, scaling, and failover but is distinct from full serverless paradigms. Evolution of Web Architectures The episode contrasts older monolithic frameworks (Django, Ruby on Rails) with current microservice and serverless architectures. Developers are encouraged to leverage modern tools where possible, adopting serverless and cloud-managed components until advanced customization requires traditional servers. Links Client for web client : quick-start React setup : CSS framework (alternatives: Tailwind, Chakra, MaterialUI) and as useful plugins for mobile apps Server for serverless web and mobile backends (underlying building blocks) for serverless functions , , , for containerized deployment Database, Job-Queues, Sessions as the primary relational database for session-management and pub/sub or for job queuing (with wrapper: )
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MLA 012 Docker for Machine Learning Workflows
11/09/2020
MLA 012 Docker for Machine Learning Workflows
Docker enables efficient, consistent machine learning environment setup across local development and cloud deployment, avoiding many pitfalls of virtual machines and manual dependency management. It streamlines system reproduction, resource allocation, and GPU access, supporting portability and simplified collaboration for ML projects. Machine learning engineers benefit from using pre-built Docker images tailored for ML, allowing seamless project switching, host OS flexibility, and straightforward deployment to cloud platforms like AWS ECS and Batch, resulting in reproducible and maintainable workflows. Links Notes and resources at stay healthy & sharp while you learn & code Traditional Environment Setup Challenges Traditional machine learning development often requires configuring operating systems, GPU drivers (CUDA, cuDNN), and specific package versions directly on the host machine. Manual setup can lead to version conflicts, resource allocation issues, and difficulty reproducing environments across different systems or between local and cloud deployments. Tools like Anaconda and "pipenv" help manage Python and package versions, but they often fall short in managing system-level dependencies such as CUDA and cuDNN. Virtual Machines vs Containers Virtual machines (VMs) like VirtualBox or VMware allow multiple operating systems to run on a host, but they pre-allocate resources (RAM, CPU) up front and have limited access to host GPUs, restricting usability for machine learning tasks. Docker uses containerization to package applications and dependencies, allowing containers to share host resources dynamically and to access the GPU directly, which is essential for ML workloads. Benefits of Docker for Machine Learning Dockerfiles describe the entire guest operating system and software environment in code, enabling complete automation and repeatability of environment setup. Containers created from Dockerfiles use only the necessary resources at runtime and avoid interfering with the host OS, making it easy to switch projects, share setups, or scale deployments. GPU support in Docker allows machine learning engineers to leverage their hardware regardless of host OS (with best results on Windows and Linux with Nvidia cards). On Windows, enabling GPU support requires switching to the Dev/Insider channel and installing specific Nvidia drivers alongside WSL2 and Nvidia-Docker. Macs are less suitable for GPU-accelerated ML due to their AMD graphics cards, although workarounds like PlaidML exist. Cloud Deployment and Reproducibility Deploying machine learning models traditionally required manual replication of environments on cloud servers, such as EC2 instances, which is time-consuming and error-prone. With Docker, the same Dockerfile can be used locally and in the cloud (AWS ECS, Batch, Fargate, EKS, or SageMaker), ensuring the deployed environment matches local development exactly. AWS ECS is suited for long-lived container services, while AWS Batch can be used for one-off or periodic jobs, offering cost-effective use of spot instances for GPU workloads. Using Pre-Built Docker Images Docker Hub provides pre-built images for ML environments, such as nvcr.io's CUDA/cuDNN images and HuggingFace's transformers setups, which can be inherited in custom Dockerfiles. These images ensure compatibility between key ML libraries (PyTorch, TensorFlow, CUDA, cuDNN) and reduce setup friction. Custom kitchen-sink images, like those in the "ml-tools" repository, offer a turnkey solution for getting started with machine learning in Docker. Project Isolation and Maintenance With Docker, each project can have a fully isolated environment, preventing dependency conflicts and simplifying switching between projects. Updates or configuration changes are tracked and versioned in the Dockerfile, maintaining a single source of truth for the entire environment. Modifying the Dockerfile to add dependencies or update versions ensures that local and cloud environments remain synchronized. Host OS Recommendations for ML Development Windows is recommended for local development with Docker, offering better desktop experience and driver support than Ubuntu for most users, particularly on laptops. GPU-accelerated ML is not practical on Macs due to hardware limitations, while Ubuntu is suitable for advanced users comfortable with system configuration and driver management. Useful Links
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MLG 032 Cartesian Similarity Metrics
11/08/2020
MLG 032 Cartesian Similarity Metrics
to stay healthy while you study or work! Show notes at . L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product Normed distances A norm is a function that assigns a strictly positive length to each vector in a vector space. Minkowski is generalized. p_root(sum(xi-yi)^p). "p" = ? (1, 2, ..) for below. L1: Manhattan/city-block/taxicab. abs(x2-x1)+abs(y2-y1). Grid-like distance (triangle legs). Preferred for high-dim space. L2: Euclidean. sqrt((x2-x1)^2+(y2-y1)^2. sqrt(dot-product). Straight-line distance; min distance (Pythagorean triangle edge) Others: Mahalanobis, Chebyshev (p=inf), etc Dot product A type of inner product. Outer-product: lies outside the involved planes. Inner-product: dot product lies inside the planes/axes involved . Dot product: inner product on a finite dimensional Euclidean space Cosine (normalized dot)
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MLA 011 Practical Clustering Tools
11/08/2020
MLA 011 Practical Clustering Tools
Primary clustering tools for practical applications include K-means using scikit-learn or Faiss, agglomerative clustering leveraging cosine similarity with scikit-learn, and density-based methods like DBSCAN or HDBSCAN. For determining the optimal number of clusters, silhouette score is generally preferred over inertia-based visual heuristics, and it natively supports pre-computed distance matrices. Links Notes and resources at stay healthy & sharp while you learn & code K-means Clustering K-means is the most widely used clustering algorithm and is typically the first method to try for general clustering tasks. The is suitable for small to medium-sized datasets, while is more efficient and accurate for very large datasets. K-means requires the number of clusters to be specified in advance and relies on the Euclidean distance metric, which performs poorly in high-dimensional spaces. When document embeddings have high dimensionality (e.g., 768 dimensions from sentence transformers), K-means becomes less effective due to the limitations of Euclidean distance in such spaces. Alternatives to K-means for High Dimensions For text embeddings with high dimensionality, agglomerative (hierarchical) clustering methods are preferable, particularly because they allow the use of different similarity metrics. in scikit-learn accepts a pre-computed cosine similarity matrix, which is more appropriate for natural language processing. Constructing the pre-computed distance (or similarity) matrix involves normalizing vectors and computing dot products, which can be efficiently achieved with linear algebra libraries like PyTorch. Hierarchical algorithms do not use inertia in the same way as K-means and instead rely on external metrics, such as silhouette score. Other clustering algorithms exist, including spectral, mean shift, and affinity propagation, which are not covered in this episode. Semantic Search and Vector Indexing Libraries such as Faiss, Annoy, and HNSWlib provide approximate nearest neighbor search for efficient semantic search on large-scale vector data. These systems create an index of your embeddings to enable rapid similarity search, often with the ability to specify cosine similarity as the metric. Sample code using these libraries with sentence transformers can be found in the . Determining the Optimal Number of Clusters Both K-means and agglomerative clustering require a predefined number of clusters, but this is often unknown beforehand. The "elbow" method involves running the clustering algorithm with varying cluster counts and plotting the inertia (sum of squared distances within clusters) to visually identify the point of diminishing returns; see . The can automatically detect the "elbow" or "knee" in the inertia plot, eliminating subjective human judgment; . The silhouette score, calculated via , considers both inter- and intra-cluster distances and allows for direct selection of the number of clusters with the maximum score. The silhouette score can be computed using a pre-computed distance matrix (such as from cosine similarities), making it well-suited for applications involving non-Euclidean metrics and hierarchical clustering. Density-Based Clustering: DBSCAN and HDBSCAN is a hierarchical clustering method that does not require specifying the number of clusters, instead discovering clusters based on data density. is a more popular and versatile implementation of density-based clustering, capable of handling various types of data without significant parameter tuning. DBSCAN and HDBSCAN can be preferable to K-means or agglomerative clustering when automatic determination of cluster count or robustness to noise is important. However, these algorithms may not perform well with all types of high-dimensional embedding data, as illustrated by the challenges faced when clustering 768-dimensional text embeddings. Summary Recommendations and Links For low- to medium-sized, low-dimensional data, use K-means with silhouette score to choose the optimal number of clusters: , . For very large data or vector search, use . For high-dimensional data using cosine similarity, use with a pre-computed square matrix of cosine similarities; . For density-based clustering, consider or . Exploratory code and further examples can be found in the .
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MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK
10/28/2020
MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK
The landscape of Python natural language processing tools has evolved from broad libraries like NLTK toward more specialized packages such as Gensim for topic modeling, SpaCy for linguistic analysis, and Hugging Face Transformers for advanced tasks, with Sentence Transformers extending transformer models to enable efficient semantic search and clustering. Each library occupies a distinct place in the NLP workflow, from fundamental text preprocessing to semantic document comparison and large-scale language understanding. Links Notes and resources at stay healthy & sharp while you learn & code Historical Foundation: NLTK ("Natural Language Toolkit") was one of the earliest and most popular Python libraries for natural language processing, covering tasks from tokenization and stemming to document classification and syntax parsing. NLTK remains a catch-all "Swiss Army knife" for NLP, but many of its functions have been supplemented or superseded by newer tools tailored to specific tasks. Specialized Topic Modeling and Phrase Analysis: Gensim emerged as the leading library for topic modeling in Python, most notably via its implementation, which groups documents according to topic distributions. Topic modeling workflows often use NLTK for initial preprocessing (tokenization, stop word removal, lemmatization), then vectorize with scikit-learn’s TF-IDF, and finally model topics with Gensim’s LDA. Gensim also provides effective , allowing the detection and combination of commonly-used word pairs or triplets (n-grams) to enhance analysis accuracy. Linguistic Structure and Manipulation: SpaCy and Related Tools is a deep-learning-based library for high-performance linguistic analysis, focusing on tasks such as part-of-speech tagging, named entity recognition, and syntactic parsing. SpaCy supports integrated sentence and word tokenization, stop word removal, and lemmatization, but for advanced lemmatization and inflection, can be used to derive proper inflections for part-of-speech tags. For even more accurate (but slower) linguistic tasks, consider via SpaCy integration as . SpaCy can examine parse trees to identify sentence components, enabling sophisticated NLP applications like grammatical corrections and intent detection in conversation agents. High-Level NLP Tasks: Hugging Face Transformers provides interfaces to transformer-based models (like BERT and its successors) capable of advanced NLP tasks including question answering, summarization, translation, and sentiment analysis. Its allow users to accomplish over ten major NLP applications with minimal code. The library’s hosts a vast collection of pre-trained models that can be used for both research and production. Semantic Search and Clustering: Sentence Transformers extends the transformer approach to create dense document embeddings, enabling semantic search, clustering, and similarity comparison via cosine distance or similar metrics. Example applications include finding the most similar documents, clustering user entries, or summarizing clusters of text. The repository offers for tasks such as semantic search and clustering, often using cosine similarity. For very large-scale semantic search (such as across Wikipedia), approximate nearest neighbor (ANN) libraries like Annoy, FAISS, and hnswlib enable rapid similarity search with embeddings; practical are provided in the Sentence Transformers documentation. Additional Resources and Library Landscape For a comparative overview and discovery of further libraries, see , which reviews several packages beyond those discussed here. Summary of Library Roles and Use Cases NLTK: Foundational and comprehensive for most classic NLP needs; still covers a broad range of preprocessing and basic analytic tasks. Gensim: Best for topic modeling and phrase extraction (bigrams/trigrams); especially useful in workflows relying on document grouping and label generation. SpaCy: Leading tool for syntactic, linguistic, and grammatical analysis; supports integration with advanced lemmatizers and external tools like Stanford CoreNLP. Hugging Face Transformers: The standard for modern, high-level NLP tasks and quick prototyping, featuring simple pipelines and an extensive model hub. Sentence Transformers: The main approach for embedding text for semantic search, clustering, and large-scale document comparison, supporting ANN methodologies via companion libraries.
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MLA 009 Charting and Visualization Tools for Data Science
11/06/2018
MLA 009 Charting and Visualization Tools for Data Science
Python charting libraries - Matplotlib, Seaborn, and Bokeh - explaining, their strengths from quick EDA to interactive, HTML-exported visualizations, and clarifies where D3.js fits as a JavaScript alternative for end-user applications. It also evaluates major software solutions like Tableau, Power BI, QlikView, and Excel, detailing how modern BI tools now integrate drag-and-drop analytics with embedded machine learning, potentially allowing business users to automate entire workflows without coding. Links Notes and resources at stay healthy & sharp while you learn & code Core Phases in Data Science Visualization Exploratory Data Analysis (EDA): EDA occupies an early stage in the Business Intelligence (BI) pipeline, positioned just before or sometimes merged with the data cleaning (“munging”) phase. The outputs of EDA (e.g., correlation matrices, histograms) often serve as inputs to subsequent machine learning steps. Python Visualization Libraries 1. Matplotlib The foundational plotting library in Python, supporting static, basic chart types. Requires substantial boilerplate code for custom visualizations. Serves as the core engine for many higher-level visualization tools. Common EDA tasks (like plotting via .corr(), .hist(), and .scatter() methods on pandas DataFrames) depend on Matplotlib under the hood. 2. Pandas Plotting Pandas integrates tightly with Matplotlib and exposes simple, one-line commands for common plots (e.g., df.corr(), df.hist()). Designed to make quick EDA accessible without requiring detailed knowledge of Matplotlib’s verbose syntax. 3. Seaborn A high-level wrapper around Matplotlib, analogous to how Keras wraps TensorFlow. Sets sensible defaults for chart styles, fonts, colors, and sizes, improving aesthetics with minimal effort. Importing Seaborn can globally enhance the appearance of all Matplotlib plots, even without direct usage of Seaborn’s plotting functions. 4. Bokeh A powerful library for creating interactive, web-ready plots from Python. Enables user interactions such as hovering, zooming, and panning within rendered plots. Exports visualizations as standalone HTML files or can operate as a server-linked app for live data exploration. Supports advanced features like cross-filtering, allowing dynamic slicing and dicing of data across multiple axes or columns. More suited for creating reusable, interactive dashboards rather than quick, one-off EDA visuals. 5. D3.js Unlike previous libraries, D3.js is a JavaScript framework for creating complex, highly customized data visualizations for web and mobile apps. Used predominantly on the client-side to build interactive front-end graphics for end users, not as an EDA tool for analysts. Common in production-grade web apps, but not typically part of a Python-based data science workflow. Dedicated Visualization and BI Software Tableau Leading commercial drag-and-drop BI tool for data visualization and dashboarding. Connects to diverse data sources (CSV, Excel, databases), auto-detects column types, and suggests default chart types. Users can interactively build visualizations, cross-filter data, and switch chart types without coding. Power BI Microsoft’s BI suite, similar to Tableau, supporting end-to-end data analysis and visualization. Integrates data preparation, visualization, and increasingly, built-in machine learning workflows. Focused on empowering business users or analysts to run the BI pipeline without programming. QlikView Another major BI offering is QlikView, emphasizing interactive dashboards and data exploration. Excel Still widely used for basic EDA and visualizations directly on spreadsheets. Offers limited but accessible charting tools for histograms, scatter plots, and simple summary statistics. Data often originates from Excel/CSV files before being ingested for further analysis in Python/pandas. Trends & Insights Workflow Integration: Modern BI tools are converging, adding both classic EDA capabilities and basic machine learning modeling, often through a code-free interface. Automation Risks and Opportunities: As drag-and-drop BI tools increase in capabilities (including model training and selection), some data science coding work traditionally required for BI pipelines may become accessible to non-programmers. Distinctions in Use: Python libraries (Matplotlib, Seaborn, Bokeh) excel in automating and scripting EDA, report generation, and static analysis as part of data pipelines. BI software (Tableau, Power BI, QlikView) shines for interactive exploration and democratized analytics, integrated from ingestion to reporting. D3.js stands out for tailored, production-level, end-user app visualizations, rarely leveraged by data scientists for EDA. Key Takeaways For quick, code-based EDA: Use Pandas’ built-in plotters (wrapping Matplotlib). For pre-styled, pretty plots: Use Seaborn (with or without direct API calls). For interactive, shareable dashboards: Use Bokeh for Python or BI tools for no-code operation. For enterprise, end-user-facing dashboards: Choose BI software like Tableau or build custom apps using D3.js for total control.
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MLA 008 Exploratory Data Analysis (EDA)
10/26/2018
MLA 008 Exploratory Data Analysis (EDA)
Exploratory data analysis (EDA) sits at the critical pre-modeling stage of the data science pipeline, focusing on uncovering missing values, detecting outliers, and understanding feature distributions through both statistical summaries and visualizations, such as Pandas' info(), describe(), histograms, and box plots. Visualization tools like Matplotlib, along with processes including imputation and feature correlation analysis, allow practitioners to decide how best to prepare, clean, or transform data before it enters a machine learning model. Links Notes and resources at stay healthy & sharp while you learn & code EDA in the Data Science Pipeline Position in Pipeline: EDA is an essential pre-processing step in the business intelligence (BI) or data science pipeline, occurring after data acquisition but before model training. Purpose: The goal of EDA is to understand the data by identifying: Missing values (nulls) Outliers Feature distributions Relationships or correlations between variables Data Acquisition and Initial Inspection Data Sources: Data may arrive from various streams (e.g., Twitter, sensors) and is typically stored in structured formats such as databases or spreadsheets. Loading Data: In Python, data is often loaded into a Pandas DataFrame using commands like pd.read_csv('filename.csv'). Initial Review: df.info(): Displays data types and counts of non-null entries by column, quickly highlighting missing values. df.describe(): Provides summary statistics for each column, including count, mean, standard deviation, min/max, and quartiles. Handling Missing Data and Outliers Imputation: Missing values must often be filled (imputed), as most machine learning algorithms cannot handle nulls. Common strategies: impute with mean, median, or another context-appropriate value. For example, missing ages can be filled with the column's average rather than zero, to avoid introducing skew. Outlier Strategy: Outliers can be removed, replaced (e.g., by nulls and subsequently imputed), or left as-is if legitimate. Treatment depends on whether outliers represent true data points or data errors. Visualization Techniques Purpose: Visualizations help reveal data distributions, outliers, and relationships that may not be apparent from raw statistics. Common Visualization Tools: Matplotlib: The primary Python library for static data visualizations. Visualization Methods: Histogram: Ideal for visualizing the distribution of a single variable (e.g., age), making outliers visible as isolated bars. Box Plot: Summarizes quartiles, median, and range, with 'whiskers' showing min/max; useful for spotting outliers and understanding data spread. Line Chart: Used for time-series data, highlighting trends and anomalies (e.g., sudden spikes in stock price). Correlation Matrix: Visual grid (often of scatterplots) comparing each feature against every other, helping to detect strong or weak linear relationships between features. Feature Correlation and Dimensionality Correlation Plot: Generated with df.corr() in Pandas to assess linear relationships between features. High correlation between features may suggest redundancy (e.g., number of bedrooms and square footage) and inform feature selection or removal. Limitations: While correlation plots provide intuition, automated approaches like Principal Component Analysis (PCA) or autoencoders are typically superior for feature reduction and target prediction tasks. Data Transformation Prior to Modeling Scaling: Machine learning models, especially neural networks, often require input features to be scaled (normalized or standardized). StandardScaler (from scikit-learn): Standardizes features, but is sensitive to outliers. RobustScaler: A variant that compresses the influence of outliers, keeping data within interquartile ranges, simplifying preprocessing steps. Summary of EDA Workflow Initial Steps: Load data into a DataFrame. Examine data types and missing values with df.info(). Review summary statistics with df.describe(). Visualization: Use histograms and box plots to explore feature distributions and detect anomalies. Leverage correlation matrices to identify related features. Data Preparation: Impute missing values thoughtfully (e.g., with means or medians). Decide on treatment for outliers: removal, imputation, or scaling with tools like RobustScaler. Outcome: Proper EDA ensures that data is cleaned, features are well-understood, and inputs are suitable for effective machine learning model training.
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MLA 007 Jupyter Notebooks
10/16/2018
MLA 007 Jupyter Notebooks
Jupyter Notebooks, originally conceived as IPython Notebooks, enable data scientists to combine code, documentation, and visual outputs in an interactive, browser-based environment supporting multiple languages like Python, Julia, and R. This episode details how Jupyter Notebooks structure workflows into executable cells - mixing markdown explanations and inline charts - which is essential for documenting, demonstrating, and sharing data analysis and machine learning pipelines step by step. Links Notes and resources at stay healthy & sharp while you learn & code Overview of Jupyter Notebooks Historical Context and Scope Jupyter Notebooks began as IPython Notebooks focused solely on Python. The project was renamed Jupyter to support additional languages - namely Julia ("JU"), Python ("PY"), and R ("R") - broadening its applicability for data science and machine learning across multiple languages. Interactive, Narrative-Driven Coding Jupyter Notebooks allow for the mixing of executable code, markdown documentation, and rich media outputs within a browser-based interface. The coding environment is structured as a sequence of cells where each cell can independently run code and display its output directly underneath. Unlike traditional Python scripts, which output results linearly and impermanently, Jupyter Notebooks preserve the stepwise development process and its outputs for later review or publication. Typical Workflow Example Stepwise Data Science Pipeline Construction Import necessary libraries: Each new notebook usually starts with a cell for imports (e.g., matplotlib, scikit-learn, keras, pandas). Data ingestion phase: Read data into a pandas DataFrame via read_csv for CSVs or read_sql for databases. Exploratory analysis steps: Use DataFrame methods like .info() and .describe() to inspect the dataset; results are rendered below the respective cell. Model development: Train a machine learning model - for example using Keras - and output performance metrics such as loss, mean squared error, or classification accuracy directly beneath the executed cell. Data visualization: Leverage charting libraries like matplotlib to produce inline plots (e.g., histograms, correlation matrices), which remain visible as part of the notebook for later reference. Publishing and Documentation Features Markdown Support and Storytelling Markdown cells enable the inclusion of formatted explanations, section headings, bullet points, and even inline images and videos, allowing for clear documentation and instructional content interleaved with code. This format makes it simple to delineate different phases of a pipeline (e.g., "Data Ingestion", "Data Cleaning", "Model Evaluation") with descriptive context. Inline Visual Outputs Outputs from code cells, such as tables, charts, and model training logs, are preserved within the notebook interface, making it easy to communicate findings and reasoning steps alongside the code. Visualization libraries (like matplotlib) can render charts directly in the notebook without the need to generate separate files. Reproducibility and Sharing Notebooks can be published to platforms like GitHub, where the full code, markdown, and most recent cell outputs are viewable in-browser. This enables transparent workflow documentation and facilitates tutorials, blog posts, and collaborative analysis. Practical Considerations and Limitations Cell-based Execution Flexibility Each cell can be run independently, so developers can repeatedly rerun specific steps (e.g., re-trying a modeling cell after code fixes) without needing to rerun the entire notebook. This is especially useful for iterative experimentation with large or slow-to-load datasets. Primary Use Cases Jupyter Notebooks excel at "storytelling" - presenting an analytical or modeling process along with its rationale and findings, primarily for publication or demonstration. For regular development, many practitioners prefer traditional editors or IDEs (like PyCharm or Vim) due to advanced features such as debugging, code navigation, and project organization. Summary Jupyter Notebooks serve as a central tool for documenting, presenting, and sharing the entirety of a machine learning or data analysis pipeline - combining code, output, narrative, and visualizations into a single, comprehensible document ideally suited for tutorials, reports, and reproducible workflows.
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MLA 006 Salaries for Data Science & Machine Learning
07/19/2018
MLA 006 Salaries for Data Science & Machine Learning
O'Reilly's 2017 Data Science Salary Survey finds that location is the most significant salary determinant for data professionals, with median salaries ranging from $134,000 in California to under $30,000 in Eastern Europe, and highlights that negotiation skills can lead to salary differences as high as $45,000. Other key factors impacting earnings include company age and size, job title, industry, and education, while popular tools and languages—such as Python, SQL, and Spark—do not strongly influence salary despite widespread use. Links Notes and resources at stay healthy & sharp while you learn & code Global and Regional Salary Differences Median Global Salary: $90,000 USD, up from $85,000 the previous year. Regional Breakdown: United States: $112,000 median; California leads at $134,000. Western Europe: $57,000—about half the US median. Australia & New Zealand: Second after the US. Eastern Europe: Below $30,000. Asia: Wide interquartile salary range, indicating high variability. Demographic and Personal Factors Gender: Women's median salaries are $8,000 lower than men's. Women make up 20% of respondents but are increasing in number. Age & Experience: Higher age/experience correlates with higher salaries, but the proportion of older professionals declines. Education: Nearly all respondents have at least a master's; PhD holders earn only about $5,000 more than those with a master’s. Negotiation Skills: Self-reported strong salary negotiation skills are linked to $45,000 higher median salaries (from $70,000 for lowest to $115,000 for highest bargaining skill). Industry, Company, and Role Industry Impact: Highest salaries found in search/social networking and media/entertainment. Education and non-profit offer the lowest pay. Company Age & Size: Companies aged 2–5 years offer higher than average pay; less than 2 years old offer much lower salaries (~$40,000). Large organizations generally pay more. Job Title: "Data scientist" and "data analyst" titles carry higher medians than "engineer" titles by around $7,000. Executive titles (CTO, VP, Director) see the highest pay, with CTOs at $150,000 median. Tools, Languages, and Technologies Operating Systems: Windows: 67% usage, but declining. Linux: 55%; Unix: 18%; macOS: 46%; Unix-based systems are rising in use. Programming Languages: SQL: 64% (most used for database querying). Python: 63% (most popular procedural language). R: 54%. Others (Java, Scala, C/C++, C#): Each less than 20%. Salary difference across languages is minor; C/C++ users earn more but not enough to outweigh the difficulty. Databases: MySQL (37%), MS SQL Server (30%), PostgreSQL (28%). Popularity of the database has little impact on pay. Big Data and Search Tools: Spark: Most popular big data platform, especially for large-scale data processing. Elasticsearch: Most common search engine, but Solr pays more. Machine Learning Libraries: Scikit-learn (37%) and Spark MLlib (16%) are most used. Visualization Tools: R’s ggplot2 and Python’s matplotlib are leading choices. Key Salary Differentiators (per Machine Learning Analysis) Top Predictors (explaining ~60% of salary variance): World/US region Experience Gender Company size Education (but amounting to only ~$5,000 difference) Job title Industry Lesser Impact: Specific tools, languages, and databases do not meaningfully affect salary. Summary Takeaways The greatest leverage for a higher salary comes from geography and individual negotiation capability, with up to $45,000 differences possible. Role/title selection, industry, company age, and size are also significant, while mastering the most commonly used tools is essential but does not strongly differentiate pay. For aspiring data professionals: focus on developing negotiation skills and, where possible, optimize for location and title to maximize earning potential.
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MLA 005 Shapes and Sizes: Tensors and NDArrays
06/09/2018
MLA 005 Shapes and Sizes: Tensors and NDArrays
Explains the fundamental differences between tensor dimensions, size, and shape, clarifying frequent misconceptions—such as the distinction between the number of features (“columns”) and true data dimensions—while also demystifying reshaping operations like expand_dims, squeeze, and transpose in NumPy. Through practical examples from images and natural language processing, listeners learn how to manipulate tensors to match model requirements, including scenarios like adding dummy dimensions for grayscale images or reordering axes for sequence data. Links Notes and resources at stay healthy & sharp while you learn & code Definitions Tensor: A general term for an array of any number of dimensions. 0D Tensor (Scalar): A single number (e.g., 5). 1D Tensor (Vector): A simple list of numbers. 2D Tensor (Matrix): A grid of numbers (rows and columns). 3D+ Tensors: Higher-dimensional arrays, such as images or batches of images. NDArray (NumPy): Stands for "N-dimensional array," the foundational array type in NumPy, synonymous with "tensor." Tensor Properties Dimensions Number of nested levels in the array (e.g., a matrix has two dimensions: rows and columns). Access in NumPy: Via .ndim property (e.g., array.ndim). Size Total number of elements in the tensor. Examples: Scalar: size = 1 Vector: size equals number of elements (e.g., 5 for [1, 2, 3, 4, 5]) Matrix: size = rows × columns (e.g., 10×10 = 100) Access in NumPy: Via .size property. Shape Tuple listing the number of elements per dimension. Example: An image with 256×256 pixels and 3 color channels has shape = (256, 256, 3). Common Scenarios & Examples Data Structures in Practice CSV/Spreadsheet Example: Dataset with 1 million housing examples and 50 features: Shape: (1_000_000, 50) Size: 50,000,000 Image Example (RGB): 256×256 pixel image: Shape: (256, 256, 3) Dimensions: 3 (width, height, channels) Batching for Models: For a convolutional neural network, shape might become (batch_size, width, height, channels), e.g., (32, 256, 256, 3). Conceptual Clarifications The term "dimensions" in data science often refers to features (columns), but technically in tensors it means the number of structural axes. The "curse of dimensionality" often uses "dimensions" to refer to features, not tensor axes. Reshaping and Manipulation in NumPy Reshaping Tensors Adding Dimensions: Useful when a model expects higher-dimensional input than currently available (e.g., converting grayscale image from shape (256, 256) to (256, 256, 1)). Use np.expand_dims or array.reshape. Removing Singleton Dimensions: Occurs when, for example, model output is (N, 1) and single dimension should be removed to yield (N,). Use np.squeeze or array.reshape. Wildcard with -1: In reshaping, -1 is a placeholder for NumPy to infer the correct size, useful when batch size or another dimension is variable. Flattening: Use np.ravel to turn a multi-dimensional tensor into a contiguous 1D array. Axis Reordering Transposing Axes: Needed when model input or output expects axes in a different order (e.g., sequence length and embedding dimensions in NLP). Use np.transpose for general axis permutations. Use np.swapaxes to swap two specific axes but prefer transpose for clarity and flexibility. Practical Example In NLP sequence models: 3D tensor with (batch_size, sequence_length, embedding_dim) might need to be reordered to (batch_size, embedding_dim, sequence_length) for certain models. Achieved using: array.transpose(0, 2, 1) Core NumPy Functions for Manipulation reshape: General function for changing the shape of a tensor, including adding or removing dimensions. expand_dims: Adds a new axis with size 1. squeeze: Removes axes with size 1. ravel: Flattens to 1D. transpose: Changes the order of axes. swapaxes: Swaps specified axes (less general than transpose). Summary Table of Operations Operation NumPy Function Purpose Add dimension np.expand_dims Convert (256,256) to (256,256,1) Remove dimension np.squeeze Convert (N,1) to (N,) General reshape np.reshape Any change matching total size Flatten np.ravel Convert (a,b) to (a*b,) Swap axes np.swapaxes Exchange positions of two axes Permute axes np.transpose Reorder any sequence of axes Closing Notes A deep understanding of tensor structure - dimensions, size, and shape - is vital for preparing data for machine learning models. Reshaping, expanding, squeezing, and transposing tensors are everyday tasks in model development, especially for adapting standard datasets and models to each other.
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MLA 003 Storage: HDF, Pickle, Postgres
05/24/2018
MLA 003 Storage: HDF, Pickle, Postgres
Practical workflow of loading, cleaning, and storing large datasets for machine learning, moving from ingesting raw CSVs or JSON files with pandas to saving processed datasets and neural network weights using HDF5 for efficient numerical storage. It clearly distinguishes among storage options—explaining when to use HDF5, pickle files, or SQL databases—while highlighting how libraries like pandas, TensorFlow, and Keras interact with these formats and why these choices matter for production pipelines. Links Notes and resources at stay healthy & sharp while you learn & code Data Ingestion and Preprocessing Data Sources and Formats: Datasets commonly originate as CSV (comma-separated values), TSV (tab-separated values), fixed-width files (FWF), JSON from APIs, or directly from databases. Typical applications include structured data (e.g., real estate features) or unstructured data (e.g., natural language corpora for sentiment analysis). Pandas as the Core Ingestion Tool: Pandas provides versatile functions such as read_csv, read_json, and others to load various file formats with robust options for handling edge cases (e.g., file encodings, missing values). After loading, data cleaning is performed using pandas: dropping or imputing missing values, converting booleans and categorical columns to numeric form. Data Encoding for Machine Learning: All features must be numerical before being supplied to machine learning models like TensorFlow or Keras. Categorical data is one-hot encoded using pandas.get_dummies, converting strings to binary indicator columns. The underlying NumPy array of a DataFrame is accessed via df.values for direct integration with modeling libraries. Numerical Data Storage Options HDF5 for Storing Processed Arrays: HDF5 (Hierarchical Data Format version 5) enables efficient storage of large multidimensional NumPy arrays. Libraries like h5py and built-in pandas functions (to_hdf) allow seamless saving and retrieval of arrays or DataFrames. TensorFlow and Keras use HDF5 by default to store neural network weights as multi-dimensional arrays for model checkpointing and early stopping, accommodating robust recovery and rollback. Pickle for Python Objects: Python's pickle protocol serializes arbitrary objects, including machine learning models and arrays, into files for later retrieval. While convenient for quick iterations or heterogeneous data, pickle is less efficient with NDarrays compared to HDF5, lacks significant compression, and poses security risks if not properly safeguarded. SQL Databases and Spreadsheets: For mixed or heterogeneous data, or when producing results for sharing and collaboration, relational databases like PostgreSQL or spreadsheets such as CSVs are used. Databases serve as the endpoint for production systems, where model outputs—such as generated recommendations or reports—are published for downstream use. Storage Workflow in Machine Learning Pipelines Typical Process: Data is initially loaded and processed with pandas, then converted to numerical arrays suitable for model training. Intermediate states and model weights are saved using HDF5 during model development and training, ensuring recovery from interruptions and facilitating early stopping. Final outputs, especially those requiring sharing or production use, are published to SQL databases or shared as spreadsheet files. Best Practices and Progression: Quick project starts may involve pickle for accessible storage during early experimentation. For large-scale, high-performance applications, migration to HDF5 for numerical data and SQL for production-grade results is recommended. Alternative options like Feather and PyTables (an interface on top of HDF5) exist for specialized needs. Summary HDF5 is optimal for numerical array storage due to its efficiency, built-in compression, and integration with major machine learning frameworks. Pickle accommodates arbitrary Python objects but is suboptimal for numerical data persistence or security. SQL databases and spreadsheets are used for disseminating results, especially when human consumption or application integration is required. The selection of a storage format is determined by data type, pipeline stage, and end-use requirements within machine learning workflows.
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MLA 002 Numpy & Pandas
05/24/2018
MLA 002 Numpy & Pandas
NumPy enables efficient storage and vectorized computation on large numerical datasets in RAM by leveraging contiguous memory allocation and low-level C/Fortran libraries, drastically reducing memory footprint compared to native Python lists. Pandas, built on top of NumPy, introduces labelled, flexible tabular data manipulation—facilitating intuitive row and column operations, powerful indexing, and seamless handling of missing data through tools like alignment, reindexing, and imputation. Links Notes and resources at stay healthy & sharp while you learn & code NumPy: Efficient Numerical Arrays and Vectorized Computation Purpose and Design NumPy ("Numerical Python") is the foundational library for handling large numerical datasets in RAM. It introduces the ndarray (n-dimensional array), which is synonymous with a tensor—enabling storage of vectors, matrices, or higher-dimensional data. Memory Efficiency NumPy arrays are homogeneous: all elements share a consistent data type (e.g., float64, int32, bool). This data type awareness enables allocation of tightly-packed, contiguous memory blocks, optimizing both RAM usage and data access speed. Memory footprint can be orders of magnitude lower than equivalent native Python lists; for example, tasks that exhausted 32GB of RAM using Python lists could drop to just 6GB with NumPy structures. Vectorized Operations NumPy supports vectorized calculations: operations (such as squaring all elements) are applied across entire arrays in a single step, without explicit Python loops. These operations are operator-overloaded and are executed by delegating instructions to low-level, highly optimized C or Fortran routines, delivering significant computational speed gains. Conditional operations and masking, such as zeroing out negative numbers (akin to a ReLU activation), can be done efficiently with Boolean masks. Pandas: Advanced Tabular Data Manipulation Relationship to NumPy Pandas builds upon NumPy, leveraging its underlying optimized array storage and computation for numerical columns in its data structures. Supports additional types like strings for non-numeric data, which are common in real-world datasets. 2D Data Handling and Directional Operations The core Pandas structure is the DataFrame, which handles labelled rows and columns, analogous to a spreadsheet or SQL table. Operations are equally intuitive row-wise and column-wise, facilitating both SQL-like ("row-oriented") and "columnar" manipulations. This dual-orientation enables many complex data transformations to be succinct one-liners instead of lengthy Python code. Indexing and Alignment Pandas uses flexible and powerful indexing, enabling functions such as joining disparate datasets via a shared index (e.g., timestamp alignment in financial time series). When merging DataFrames (e.g., two stocks with differing trading days), Pandas automatically aligns data on the index, introducing NaN (null) values for unmatched dates. Handling Missing Data (Imputation) Pandas includes robust features for detecting and filling missing values, known as imputation. Options include forward filling, backfilling, or interpolating missing values based on surrounding data. Datasets can be reindexed against standardized sequences, such as all valid trading days, to enforce consistent time frames and further identify or fill data gaps. Use Cases and Integration Pandas simplifies ETL (extract, transform, load) for CSV and database-derived data, merging NumPy’s computation power with tools for advanced data cleaning and integration. When preparing data for machine learning frameworks (e.g., TensorFlow or Keras), Pandas DataFrames can be converted back into NumPy arrays for computation, maintaining tight integration across the data science stack. Summary: NumPy underpins high-speed numerical operations and memory efficiency, while Pandas extends these capabilities to powerful, flexible, and intuitive manipulation of labelled multi-dimensional data -together forming the backbone of data analysis and preparation in Python machine learning workflows.
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MLA 001 Degrees, Certificates, and Machine Learning Careers
05/24/2018
MLA 001 Degrees, Certificates, and Machine Learning Careers
While industry-respected credentials like Udacity Nanodegrees help build a practical portfolio for machine learning job interviews, they remain insufficient stand-alone qualifications—most roles require a Master’s degree as a near-hard requirement, especially compared to more flexible web development fields. A Master’s, such as Georgia Tech’s OMSCS, not only greatly increases employability but is strongly recommended for those aiming for entry into machine learning careers, while a PhD is more appropriate for advanced, research-focused roles with significant time investment. Links Notes and resources at Online Certificates: Usefulness and Limitations Udacity Nanodegree Provides valuable hands-on experience and a practical portfolio of machine learning projects. Demonstrates self-motivation and the ability to self-teach. Not industry-recognized as a formal qualification—does not by itself suffice for job placement in most companies. Best used as a supplement to demonstrate applied skills, especially in interviews where coding portfolios (e.g., on GitHub) are essential. Coursera Specializations Another MOOC resource similar to Udacity, but Udacity's Nanodegree is cited as closer to real-world relevance among certificates. Neither is accredited or currently accepted as a substitute for formal university degrees by most employers. The Role of a Portfolio Possessing a portfolio with multiple sophisticated projects is critical, regardless of educational background. Interviewers expect examples showcasing data processing (e.g., with Pandas and NumPy), analysis, and end-to-end modeling using libraries like scikit-learn or TensorFlow. Degree Requirements in Machine Learning Bachelor’s Degree Often sufficient for software engineering and web development roles but generally inadequate for machine learning positions. In web development, non-CS backgrounds and bootcamp graduates are commonplace; the requirement is flexible. Machine learning employers treat “Master’s preferred” as a near-required credential, sharply contrasting with the lax standards in web and mobile development. Master’s Degree Significantly improves employability and is typically expected for most machine learning roles. The Georgia Tech Online Master of Science in Computer Science (OMSCS) is highlighted as a cost-effective, flexible, and industry-recognized path. Industry recruiters often filter out candidates without a master's, making advancement with only a bachelor’s degree an uphill struggle. A master's degree reduces obstacles and levels the playing field with other candidates. PhD Necessary mainly for highly research-centric positions at elite companies (e.g., Google, OpenAI). Opens doors to advanced research and high salaries (often $300,000+ per year in leading tech sectors). Involves years of extensive commitment; suitable mainly for those with a passion for research. Recommendations For Aspiring Machine Learning Professionals: Start with a bachelor’s if you don’t already have one. Strongly consider a master’s degree (such as OMSCS) for solid industry entry. Only pursue a PhD if intent on working in cutting-edge research roles. Always build and maintain a robust portfolio to supplement academic achievements. Summary Insight: A master’s degree is becoming the de facto entry ticket to machine learning careers, with MOOCs and portfolios providing crucial, but secondary, support.
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MLG 029 Reinforcement Learning Intro
02/05/2018
MLG 029 Reinforcement Learning Intro
Notes and resources: to stay healthy while you study or work! Reinforcement Learning (RL) is a fundamental component of artificial intelligence, different from purely being AI itself. It is considered a key aspect of AI due to its ability to learn through interactions with the environment using a system of rewards and punishments. Links: Concepts and Definitions Reinforcement Learning (RL): RL is a framework where an "agent" learns by interacting with its environment and receiving feedback in the form of rewards or punishments. It is part of the broader machine learning category, which includes supervised and unsupervised learning. Unlike supervised learning, where a model learns from labeled data, RL focuses on decision-making and goal achievement. Comparison with Other Learning Types Supervised Learning: Involves a teacher-student paradigm where models are trained on labeled data. Common in applications like image recognition and language processing. Unsupervised Learning: Not commonly used in practical applications according to the experience shared in the episode. Reinforcement Learning vs. Supervised Learning: RL allows agents to learn independently through interaction, unlike supervised learning where training occurs with labeled data. Applications of Reinforcement Learning Games and Simulations: Deep reinforcement learning is used in games like Go (AlphaGo) and video games, where the environment and possible rewards or penalties are predefined. Robotics and Autonomous Systems: Examples include robotics (e.g., Boston Dynamics mules) and autonomous vehicles that learn to navigate and make decisions in real-world environments. Finance and Trading: Utilized for modeling trading strategies that aim to optimize financial returns over time, although breakthrough performance in trading isn’t yet evidenced. RL Frameworks and Environments Framework Examples: OpenAI Baselines, TensorForce, and Intel's Coach, each with different capabilities and company backing for development. Environments: OpenAI's Gym is a suite of environments used for training RL agents. Future Aspects and Developments Model-based vs. Model-free RL: Model-based RL involves planning and knowledge of the world dynamics, while model-free is about reaction and immediate responses. Remaining Challenges: Current hurdles in AI include reasoning, knowledge representation, and memory, where efforts are ongoing in institutions like Google DeepMind for further advancement.
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MLG 028 Hyperparameters 2
02/04/2018
MLG 028 Hyperparameters 2
Notes and resources: to stay healthy while you study or work! More hyperparameters for optimizing neural networks. A focus on regularization, optimizers, feature scaling, and hyperparameter search methods. Hyperparameter Search Techniques Grid Search involves testing all possible permutations of hyperparameters, but is computationally exhaustive and suited for simpler, less time-consuming models. Random Search selects random combinations of hyperparameters, potentially saving time while potentially missing the optimal solution. Bayesian Optimization employs machine learning to continuously update and hone in on efficient hyperparameter combinations, avoiding the exhaustive or random nature of grid and random searches. Regularization in Neural Networks L1 and L2 Regularization penalize certain parameter configurations to prevent model overfitting; often smoothing overfitted parameters. Dropout randomly deactivates neurons during training to ensure the model doesn’t over-rely on specific neurons, fostering better generalization. Optimizers Optimizers like Adam, which combines elements of momentum and adaptive learning rates, are explained as vital tools for refining the learning process of neural networks. Adam, being the most sophisticated and commonly used optimizer, improves upon simpler techniques like momentum by incorporating more advanced adaptative features. Initializers The importance of weight initialization is underscored with methods like uniform random initialization and the more advanced Xavier initialization to prevent neural networks from starting in 'stuck' states. Feature Scaling Different scaling methods such as standardization and normalization are used to scale feature inputs to small, standardized ranges. Batch Normalization is highlighted, integrating scaling directly into the network to prevent issues like exploding and vanishing gradients through the normalization of layer outputs. Links
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MLG 027 Hyperparameters 1
01/28/2018
MLG 027 Hyperparameters 1
Full notes and resources at to stay healthy while you study or work! Hyperparameters are crucial elements in the configuration of machine learning models. Unlike parameters, which are learned by the model during training, hyperparameters are set by humans before the learning process begins. They are the knobs and dials that humans can control to influence the training and performance of machine learning models. Definition and Importance Hyperparameters differ from parameters like theta in linear and logistic regression, which are learned weights. They are choices made by humans, such as the type of model, number of neurons in a layer, or the model architecture. These choices can have significant effects on the model's performance, making them vital to conscious and informed tuning. Types of Hyperparameters Model Selection: Choosing what model to use is itself a hyperparameter. For example, deciding between linear regression, logistic regression, naive Bayes, or neural networks. Architecture of Neural Networks: Number of Layers and Neurons: Deciding the width (number of neurons) and depth (number of layers). Types of Layers: Whether to use LSTMs, convolutional layers, or dense layers. Activation Functions: They transform linear outputs into non-linear outputs. Popular choices include ReLU, tanh, and sigmoid, with ReLU being the default for most neural network layers. Regularization and Optimization: These influence the learning process. The use of L1/L2 regularization or dropout, as well as the type of optimizer (e.g., Adam, Adagrad), are hyperparameters. Optimization Techniques Techniques like grid search, random search, and Bayesian optimization are used to systematically explore combinations of hyperparameters to find the best configuration for a given task. While these methods can be computationally expensive, they are necessary for achieving optimal model performance. Challenges and Future Directions The field strives towards simplifying the choice of hyperparameters, ideally automating them to become parameters of the model itself. Efforts like Google's AutoML aim to handle hyperparameter tuning automatically. Understanding and optimizing hyperparameters is a cornerstone in machine learning, directly impacting the effectiveness and efficiency of a model. Progress continues to integrate these choices into model training, reducing the dependency on human intervention and trial-and-error experimentation. Decision Tree Model selection Unsupervised? K-means Clustering => DL Linear? Linear regression, logistic regression Simple? Naive Bayes, Decision Tree (Random Forest, Gradient Boosting) Little data? Boosting Lots of data, complex situation? Deep learning Network Layer arch Vision? CNN Time? LSTM Other? MLP Trading LSTM => CNN decision Layer size design (funnel, etc) Face pics From BTC episode Don't know? Layers=1, Neurons=mean(inputs, output) Output Sigmoid = predict probability of output, usually at output Softmax = multi-class Nothing = regression Relu family (Leaky Relu, Elu, Selu, ...) = vanishing gradient (gradient is constant), performance, usually better Tanh = classification between two classes, mean 0 important
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MLG 026 Project Bitcoin Trader
01/27/2018
MLG 026 Project Bitcoin Trader
to stay healthy while you study or work! Ful notes and resources at NOTE. This episode is no longer relevant, and tforce_btc_trader no longer maintained. The current podcast project is Gnothi. Episode Overview Project: Trading Crypto Special: Intuitively highlights decisions: hypers, supervised v reinforcement, LSTM v CNN Crypto (v stock) Bitcoin, Ethereum, Litecoin, Ripple Many benefits (immutable permenant distributed ledger; security; low fees; international; etc) For our purposes: popular, volatile, singular Singular like Forex vs Stock (instruments) Trading basics Day, swing, investing Patterns (technical analysis, vs fundamentals) OHLCV / Candles Indicators Exchanges & Arbitrage (GDAX, Krakken) Good because highlights lots LSTM v CNN Supervised v Reinforcement Obvious net architectures (indicators, time-series, tanh v relu) Episode Summary The project "Bitcoin Trader" involves developing a Bitcoin trading bot using machine learning to capitalize on the hot topic of cryptocurrency and its potential profitability. The project will serve as a medium to delve into complex machine learning engineering topics, such as hyperparameter selection and reinforcement learning, over subsequent episodes. Cryptocurrency, specifically Bitcoin, is used for its universal and decentralized nature, akin to a digital, secure, and democratic financial instrument like the US dollar. Bitcoin mining involves running complex calculations to manage the currency's existence, similar to a distributed Federal Reserve system, with transactions recorded on a secure and permanent ledger known as the blockchain. The flexibility of cryptocurrency trading allows for machine learning applications across unsupervised, supervised, and reinforcement learning paradigms. This project will focus on using models such as LSTM recurrent neural networks and convolutional neural networks, highlighting Bitcoin’s unique capacity to illustrate machine learning concept decisions like network architecture. Trading differs from investing by focusing on profit from price fluctuations rather than a belief in long-term value increase. It involves understanding patterns in price actions to buy low and sell high. Different types of trading include day trading, which involves daily buying and selling, and swing trading, which spans longer periods. Trading decisions rely on patterns identified in price graphs, using time series data. Data representation through candlesticks (OHLCV: open-high-low-close-volume), coupled with indicators like moving averages and RSI, provide multiple input features for machine learning models, enhancing prediction accuracy. Exchanges like GDAX and Kraken serve as platforms for converting traditional currencies into cryptocurrencies. The efficient market hypothesis suggests that the value of an instrument is fairly priced based on the collective analysis of market participants. Differences in exchange prices can provide opportunities for arbitrage, further fueling trading strategies. The project code, currently using deep reinforcement learning via tensor force, employs convolutional neural networks over LSTM to adapt to Bitcoin trading's intricacies. The project will be available at ocdevel.com for community engagement, with future episodes tackling hyperparameter selection and deep reinforcement learning techniques.
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