feature pipelines, experiment tracking, and registries

Repeatable ML work needs named features, tracked runs, saved artifacts, and registry decisions. This chapter turns the notebook trail into a product-shaped handoff.

The exercises use small Python dictionaries and lists so every check can run in the browser. Real-world tools may be larger, but the review shape stays the same: input, decision, evidence, blocker, and next step.

By the end of the chapter, learners should be able to turn this topic into a concrete handoff instead of a vague model claim.