Package, CI-gate & observe a P5 model
P43.production-on-ml.01 · Audience: guest, it-ml, language-pro · Prerequisites: Applied Case — The Honest Tabular Model, End to End, Pipelines as code & quality gates, Metrics & the three-pillars model
The cross-discipline capstone. You take a trained model from the Data Science pillar (P5) and apply the whole Production Engineering discipline to it, end to end — the proof that the P35 → P36 → P37 skills transfer to a real artefact from another discipline. Everything runs in Pyodide: the model is given (a deterministic stub for a fitted pipeline), and the CI and request-pipeline simulators are inlined, so you engineer the model without a kernel.
ⓘ Concept: Engineer the model, don't rebuild it
predict here). The modelling is P5's job; your job is to make it shippable — packaged, gated, observable — the same production discipline you built for a library, now applied to a model.Why it matters — Most production ML work is the engineering around a model, not the modelling — so the model is given, and the discipline is everything that makes it shippable and observable.
Ask the mentor about this module
Ask a question about this content. The mentor explains and grounds its answer in what you are studying; asking is recorded as a learning signal, not a grade.
🎓 Practice ladder
4 graded rungs · ~90 minPackage the model, gate it in CI on a quality floor, make its serving observable, then assemble a production-ready model service in the capstone project.
Rung 1 — package the model (P35)
Loading exercise…
Rung 2 — CI-gate the model (P36)
Loading exercise…
Rung 3 — observe the model in serving (P37)
Loading exercise…
Rung 4 — capstone: a production-ready model service (project)
Loading exercise…
That's the discipline's flagship transfer task: a Data Science model, engineered like production Python — packaged, gated, observed, and resilient.