ML in Practice — Lightweight MLOps

The course that turns a trained model into a working production system. Reproducibility (MLflow, DVC), feature stores, model registry, batch vs online serving, FastAPI + Docker deployment, scaling and latency, A/B testing, drift detection, retraining strategy, and incident response. Six end-to-end cases mirroring what small ML teams actually build. Builds on ML Foundations; lightweight enough for teams without dedicated platform engineering.