mlops-coding-course

Why we included this project

Most ML tutorials stop at training a model in a notebook, but this course goes further and treats the codebase itself as the deliverable. It walks through the engineering side of machine learning with Python, showing how tools like uv, Ruff, pytest, and MLflow fit into a real project workflow. Each chapter comes with hands-on project instructions, so the material sticks because you actually build something with it. It's aimed at developers and data scientists who already know the ML basics but haven't yet picked up the discipline that keeps a project maintainable after the demo works. The course also links to companion example packages and templates, which is handy if your team is trying to standardize its own MLOps setup.