MLG 035 Large Language Models 2
Machine Learning Guide
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...
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MLG 034 Large Language Models 1
Machine Learning Guide
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...
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MLA 024 Code AI MCP Servers, ML Engineering
Machine Learning Guide
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...
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MLA 023 Code AI Models & Modes
Machine Learning Guide
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...
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MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf
Machine Learning Guide
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...
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MLG 033 Transformers
Machine Learning Guide
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...
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MLA 021 Databricks: Cloud Analytics and MLOps
Machine Learning Guide
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...
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MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
Machine Learning Guide
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...
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MLA 019 Cloud, DevOps & Architecture
Machine Learning Guide
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...
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MLA 017 AWS Local Development Environment
Machine Learning Guide
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...
info_outlineDatabricks 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 ocdevel.com/mlg/mla-21
- Try a walking desk 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.