MLOps Coding Course
A course that teaches software engineering practices for machine learning projects in Python, covering environment setup, prototyping, and deployment with tools such as uv, Ruff, MLflow, and Docker.
The MLOps Coding Course teaches the intersection of software engineering and data science, focused on building a production-grade MLOps codebase in Python. It is aimed at both newcomers and experienced practitioners who want to apply engineering discipline to machine learning and AI projects.
Chapters move from initializing a project and managing Python versions and dependencies, through prototyping with Jupyter notebooks, to packaging and deploying models. The course points to companion projects: an example MLOps Python package, an example LLMOps package, a cookiecutter template for building and deploying Python packages and Docker images, and a set of agent skills that help an AI coding assistant follow the course's practices.
It uses tools such as uv, Ruff, ty, pytest, MLflow, mise, and lefthook, and its content is licensed under CC-BY-4.0.