awesome-mlops

Why we included this project

This is a well-maintained index of MLOps resources that helps engineers and team leads find practical references without wading through scattered blog posts and repos. It spans the whole machine learning lifecycle, from workflow management and feature stores to deployment, monitoring, and infrastructure, and it also collects books, papers, talks, and material on the economics and governance of ML. That mix makes it useful both for hands-on tooling decisions and for understanding the broader context around production systems. Since it is a curated list rather than a product, you will still need to evaluate the candidates yourself, but it gives you a solid map to start from.