MLA 020 Kubeflow
Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker) Dirk-Jan Verdoorn - Data Scientist at Dept Agency Kubeflow. (From the w...
MLA 019 DevOps
Chatting with co-workers about the role of DevOps in a machine learning engineer's life Expert coworkers at Dept Matt Merrill - Principal Software Dev...
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 mani...
MLA 017 AWS Local Development
Show notes: ocdevel.com/mlg/mla-17 Developing on AWS first (SageMaker or other) Consider developing against AWS as your local development environment,...
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 MadeWithML for an overview ...
MLA 015 SageMaker 1
Show notes Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for a...
MLA 014 Machine Learning Server
Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev
MLA 012 Docker
Use Docker for env setup on localhost & cloud deployment, instead of pyenv / Anaconda. I recommend Windows for your desktop.