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_outlineTool 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 ocdevel.com/mlg/mla-24
- Try a walking desk 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.