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.
info_outlineTry a walking desk to stay healthy while you study or work!
Notes and resources at ocdevel.com/mlg/24
Hardware
Desktop if you're stationary, as you'll get the best performance bang-for-buck and improved longevity; laptop if you're mobile.
Desktops. Build your own PC, better value than pre-built. See PC Part Picker, make sure to use an Nvidia graphics card. Generally shoot for 2nd-best of CPUs/GPUs. Eg, RTX 4070 currently (2024-01); better value-to-price than 4080+.
For laptops, see this post (updated).
OS / Software
Use Linux (I prefer Ubuntu), or Windows, WSL2, and Docker. See mla/12 for details.
Programming Tech Stack
Deep-learning frameworks. You'll use both TF & PT eventually, so don't get hung up. mlg/9 for details.
- Tensorflow (and/or Keras)
- PyTorch (and/or Lightning)
Shallow-learning / utilities: ScikitLearn, Pandas, Numpy
Cloud-hosting: AWS / GCP / Azure. mla/13 for details.
Episode Summary
The episode discusses setting up a tech stack tailored for machine learning, emphasizing the necessity of choosing a primary programming language and framework, which, in this case, are Python and TensorFlow. The decision is supported by the ongoing popularity and community support for these tools. This preference is further influenced by the necessity for GPU optimization, which TensorFlow provides, allowing for enhanced performance through utilizing Nvidia's CUDA technology.
A notable change in the landscape is the decline of certain deep learning frameworks such as Theano, and the rise of competitors like PyTorch, which is gaining traction due to its ease of use in comparison to TensorFlow. The author emphasizes the importance of selecting frameworks with robust community support and resources, highlighting TensorFlow's lead in the market in this respect.
For hardware, the suggestion is a custom-built PC with a powerful Nvidia GPU, such as the 1080 TI, running Ubuntu Linux for best compatibility. However, for those who favor cloud services, Amazon Web Services (AWS) and Google Cloud Platform (GCP) are viable options, with a preference for GCP due to cost and performance benefits, particularly with the upcoming Tensor Processing Units (TPUs).
On the software side, the use of Pandas for data manipulation, NumPy for mathematical operations, and Scikit-Learn for shallow learning tasks provides a comprehensive toolkit for machine learning development. Additionally, the use of abstraction libraries such as Keras for simplifying TensorFlow syntax and TensorForce for reinforcement learning are recommended.
The episode further explores system architectures, suggesting a separation of concerns between a web app server and a machine learning (job) server. Communication between these components can be efficiently managed using a message queuing system like RabbitMQ, with Celery as a potential abstraction layer.
To support developers in implementing their machine learning pipelines, the recommendation extends to leveraging existing datasets, using Scikit-Learn for convenient access, and standardizing data for effective training results. The author points to several books and resources to assist in understanding and applying these technologies effectively, ending with your own workstation recommendations and building TensorFlow from source for performance gains as a potential advanced optimization step.