In this workshop, you’ll implement an opinionated MLOps stack on Databricks: feature engineering patterns, MLflow experiment tracking, model registry with promotion gates and managed endpoints with blue-green or canary rollout. You will wire up CI/CD pipelines that run eval suites (quality, robustness, fairness screens) and automatically block risky promotions.

You’ll add drift/quality monitors, alerting, and rollback runbooks, plus cost controls that right-size clusters and endpoints by workload. The result: faster iteration with fewer incidents and a transparent “evidence pack” leaders and auditors trust.