Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.
https://ocdevel.com/mlg
Gesamtlänge aller Episoden: 1 day 15 hours 40 minutes
episode 52: MLA 020 Kubeflow
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...
episode 51: MLA 019 DevOps
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)
episode 50: MLA 018 Descript
(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...
episode 49: MLA 017 AWS Local Development
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...
episode 48: MLA 016 SageMaker 2
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.)
episode 47: MLA 015 SageMaker 1
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...
episode 46: MLA 014 Machine Learning Server
Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev
episode 44: MLA 012 Docker
Use Docker for env setup on localhost & cloud deployment, instead of pyenv / Anaconda. I recommend Windows for your desktop.