Stanford University (Chip Huyen) · on GitHub
Chip Huyen's course notes and slides on designing reliable, scalable ML systems: data engineering, feature stores, training infrastructure, deployment, monitoring, and continual learning, later expanded into her O'Reilly book. Read it to think like an ML systems designer rather than a model trainer.
Engineers and ML practitioners. This is an advanced course in MLOps & AI Engineering, running about 10 weeks of slides and notes.
Free. Winter 2022 edition; slides and extensive notes public, lecture videos not publicly released.
Noah Gift and Alfredo Deza teach Python for DevOps, cloud ML platforms, MLOps tooling, and LLMOps across four courses. Suited to engineers with some coding background who want a university-branded MLOps credential.
Databricks' free role-based path takes you from getting started with ML on the platform through data preparation, model development, deployment, and MLOps with MLflow. Useful for engineers whose employers run on Databricks or who want badge-backed lakehouse MLOps skills.
Covers the full lifecycle of a deep learning product: infrastructure and tooling, troubleshooting, data management, deployment, monitoring, and ethics, with labs building a text recognizer app. Ideal for ML engineers who can train a model but need to ship one.