awesome-mlops

Why we included this project

Putting together an MLOps stack means wading through a lot of overlapping projects, and this list is a solid place to start sorting them. It groups hundreds of projects by where they fit in the machine learning lifecycle, from AutoML and feature stores to model serving, drift detection, and CI/CD for ML, so you can compare options for one stage at a time instead of running a dozen GitHub searches. Beyond the tools themselves, it collects articles, books, podcasts, and events, which helps when you want both a shortlist and some background reading. Since it's a community-maintained directory rather than software you deploy, treat it as a discovery aid for planning or upgrading a production pipeline.