MLA 022 Code AI Tools
Machine Learning Guide
Try a walking desk while studying ML or working on your projects! Show notes: Tools discussed: Windsurf: Copilot: Cursor: Cline: Roo Code: Aider: Other: Leaderboards: Video of speed-demon: Reddit: Boost programming productivity by acting as a pair programming partner. Groups these tools into three categories: • Hands-Off Tools: These include solutions that work on fixed monthly fees and require minimal user intervention. GitHub Copilot started with simple tab completions and now offers an agent mode similar to Cursor, which stands out for its advanced codebase indexing and...
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MLG 033 Transformers
Machine Learning Guide
Try a walking desk while studying ML or working on your projects! Show notes: 3Blue1Brown videos: 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...
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MLA 021 Databricks
Machine Learning Guide
Try a walking desk while studying ML or working on your projects! Discussing Databricks with Ming Chang from (part of )
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MLA 020 Kubeflow
Machine Learning Guide
Try a walking desk while studying ML or working on your projects! 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...
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MLA 019 DevOps
Machine Learning Guide
Try a walking desk while studying ML or working on your projects! 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 018 Descript
Machine Learning Guide
Try a walking desk while studying ML or working on your projects! (Optional episode) just showcasing a cool application using machine learning Dept uses Descript for some of their podcasting. I'm using it like a maniac, I think they're surprised at how into it I am. Check out the transcript & see how it performed. How to ship software, from the front lines. We talk with software developers about their craft, developer tools, developer productivity and what makes software development awesome. Hosted by your friends at Rocket Insights. AKA shipit.io An agency podcast with...
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MLA 017 AWS Local Development
Machine Learning Guide
while studying ML or working on your projects! 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
while studying ML or working on your projects! 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
while studying ML or working on your projects! 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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MLA 014 Machine Learning Server
Machine Learning Guide
while studying ML or working on your projects! Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev
info_outlineTry a walking desk while studying ML or working on your projects!
Reboot on the MLG episode, with more confident recommends.