mlops-python-package

Why we included this project

Every new machine learning project seems to start by rebuilding the same scaffolding: experiment tracking, data validation, linting, packaging, CI. This template bundles those pieces into one cloneable code base. MLflow covers experiment tracking, the model registry, and evaluation, while Pandera and Pydantic validate data and configuration; ruff and pytest keep code quality in check, and uv, Docker, and git hooks handle reproducible builds and CI/CD. The value is the opinionated structure that has these tools already wired together, so you can run the whole thing and swap in your own compute or registry choices when you outgrow the defaults. Teams standing up their first MLOps stack, or maintaining pipelines that have grown into a tangle of ad-hoc scripts, will save days by starting here and deleting what they do not need.