This is the direct sequel to Course 02. That course got a model behind an interface, in a container, on a cluster, with a rollback you could defend. This one automates everything after that moment: the pipeline that retrains it, the registry that promotes it, the store that serves its features, and the monitor that tells you it has quietly gone wrong. Serving, containers, clusters and rollout mechanics are assumed here, not re-taught. If you are maintaining a model by hand – retraining when somebody complains, from a folder of notebooks with dates in the filenames – you already know it does not scale. What you have probably been told is that you need MLOps, without being told what that consists of. It consists of four things, and this course builds all four from the primitive upward.
It takes a position on tools, out loud, in the first sections. MLflow is the one taught properly: the tracking API, what a run actually is, params against metrics against artefacts, the backing store, and the registry built on top of it. Weights and Biases gets one honest comparison lecture instead of a second shallow tutorial – what hosted collaboration, sweeps and reports buy you, what they cost in account, data residency and per-seat billing, and how to decide. Airflow is taught properly because you can run it; TFX is taught as an architecture and a set of guarantees. Two tools taught badly is worse than one taught well. Nothing here walks a console. GitHub Actions, a tracking UI, a cloud registry and a monitoring dashboard all get redesigned on somebody else’s schedule. You learn the workflow YAML, the API call and the object model, because those have been stable for years and a screenshot of a settings page is obsolete before the course finishes rendering.
