Package, CI-gate & observe a P5 model

A trained model is not yet a product: until it is packaged, quality-gated and observable, nobody can install it, trust a new version of it, or watch it serve. This cross-discipline capstone closes that gap — you take a tabular model trained in the Data Science pillar (P5) and apply the whole Production Engineering discipline to it, end to end, proving the P35 → P36 → P37 skills transfer to a real artefact from another discipline. Everything runs in Pyodide, the Python that executes inside your browser: the model is given (a deterministic stand-in for a fitted pipeline), and the CI and request-pipeline simulators are inlined in the exercises, so you engineer the model without a server-side kernel.

That's the discipline's flagship transfer task: a Data Science model, engineered like production Python — packaged, gated, observed, and resilient.