This is the phase that separates "did a Kaggle notebook" from "shipped ML." Make runs reproducible and tracked, package the model behind a REST API, put it in a container, gate it with CI/CD, deploy it — then monitor for the drift that quietly kills every model. Resiliency and observability, practised end-to-end. No GPU needed.
Take your Phase 3 churn model (or the CV / intrusion model) and ship the whole lifecycle: data pipeline → MLflow tracking → packaged model → FastAPI service → Docker → CI/CD (GitHub Actions) → deployed → monitoring + drift (Evidently). Document every stage in the README — a standout resume item most candidates lack, because most stop at "the model works in a notebook." Optionally finish MLOps Zoomcamp or Made With ML for the certificate. Ready for Phase 8 when you can take any model from notebook → versioned, tested, deployed, monitored service — and explain every stage.
Full guide: phase-7-mlops.md ↗ · architect track: Phase 9 — AI Solution Architecture.