MLA 024 Code AI MCP Servers, ML Engineering
Machine Learning Guide
Links Notes and resources at stay healthy & sharp while you learn & code audio/video editing with AI power-tools Tool Use in AI Code Agents File Operations: Agents can read, edit, and search files using sophisticated regular expressions. Executable Commands: They can recommend and perform installations like pip or npm installs, with user approval. Browser Integration: Allows agents to perform actions and verify outcomes through browser interactions. Model Context Protocol (MCP) Standardization: MCP was created by Anthropic to standardize how AI tools...
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MLA 023 Code AI Models & Modes
Machine Learning Guide
Links Notes and resources at stay healthy & sharp while you learn & code audio/video editing with AI power-tools 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...
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MLA 022 Code AI Tools
Machine Learning Guide
Links Notes and resources at stay healthy & sharp while you learn & code audio/video editing with AI power-tools I currently favor Roo Code. Plus either gemini-2.5-pro-exp-03-25 for Architect, Boomerang, or Code with large contexts. And Claude 3.7 for code with small contexts, eg Boomerang subtasks. Many others favor Cursor, Aider, or Cline. Copilot and Windsurf are less vogue lately. I found Copilot to struggle more; and their pricing - previously their winning point - is less compelling now. Why I favor Roo. The default settings have it as stable and effective as Cline, Cursor....
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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
Machine Learning Guide
to stay healthy while you study or work! Full notes at Raybeam and Databricks: Ming Chang from Raybeam discusses Raybeam's focus on data science and analytics, and how their recent acquisition by Dept Agency has expanded their scope into ML Ops and AI. Raybeam often utilizes Databricks due to its comprehensive nature. Understanding Databricks: Contrary to initial assumptions, Databricks is not just an analytics platform like Tableau but an ML Ops platform competing with tools like SageMaker and Kubeflow. It offers functionalities for creating notebooks, executing Python code, and using a...
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MLA 020 Kubeflow
Machine Learning Guide
to stay healthy while you study or work! Full notes at Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker) - Data Scientist at Dept Agency . (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to...
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MLA 019 DevOps
Machine Learning Guide
to stay healthy while you study or work! Full notes at Chatting with co-workers about the role of DevOps in a machine learning engineer's life Expert coworkers at Dept - Principal Software Developer - DevOps Lead (where Matt features often) Devops tools Pictures (funny and serious)
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MLA 017 AWS Local Development
Machine Learning Guide
to stay healthy while you study or work! Show notes: Developing on AWS first (SageMaker or other) Consider developing against AWS as your local development environment, rather than only your cloud deployment environment. Solutions: Stick to AWS Cloud IDEs (, , Connect to deployed infrastructure via Infrastructure as Code
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MLA 016 SageMaker 2
Machine Learning Guide
to stay healthy while you study or work! Full note at Part 2 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See for an overview of tooling (also generally a great ML educational run-down.)
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MLA 015 SageMaker 1
Machine Learning Guide
to stay healthy while you study or work! Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See for an overview of tooling (also generally a great ML educational run-down.) And I forgot to mention , I'll mention next time.
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- Notes and resources at ocdevel.com/mlg/mla-24
- Try a walking desk stay healthy & sharp while you learn & code
- Try Descript audio/video editing with AI power-tools
Tool Use in AI Code Agents
- File Operations: Agents can read, edit, and search files using sophisticated regular expressions.
- Executable Commands: They can recommend and perform installations like
pipornpminstalls, with user approval. - Browser Integration: Allows agents to perform actions and verify outcomes through browser interactions.
Model Context Protocol (MCP)
- Standardization: MCP was created by Anthropic to standardize how AI tools and agents communicate with each other and with external tools.
- Implementation:
- MCP Client: Converts AI agent requests into structured commands.
- MCP Server: Executes commands and sends structured responses back to the client.
- Local and Cloud Frameworks:
- Local (S-T-D-I-O MCP): Examples include utilizing Playwright for local browser automation and connecting to local databases like Postgres.
- Cloud (SSE MCP): SaaS providers offer cloud-hosted MCPs to enhance external integrations.
Expanding AI Capabilities with MCP Servers
- Directories: Various directories exist listing MCP servers for diverse functions beyond programming. modelcontextprotocol/servers
- Use Cases:
- Automation Beyond Coding: Implementing MCPs that extend automation into non-programming tasks like sales, marketing, or personal project management.
- Creative Solutions: Encourages innovation in automating routine tasks by integrating diverse MCP functionalities.
AI Tools in Machine Learning
- Automating ML Process:
- Auto ML and Feature Engineering: AI tools assist in transforming raw data, optimizing hyperparameters, and inventing new ML solutions.
- Pipeline Construction and Deployment: Facilitates the use of infrastructure as code for deploying ML models efficiently.
- Active Experimentation:
- Jupyter Integration Challenges: While integrations are possible, they often lag and may not support the latest models.
- Practical Strategies: Suggests alternating between Jupyter and traditional Python files to maximize tool efficiency.
- Action Plan for ML Engineers:
- Setup structured folders and documentation to leverage AI tools effectively.
- Encourage systematic exploration of MCPs to enhance both direct programming tasks and associated workflows.