Databricks MLOps Playbook: From MLflow to Production
This playbook distills a pragmatic MLOps path on Databricks: from data prep to robust deployment with guardrails. Why another MLOps guide? Focus on operational reality: lineage, reproducibility, cost/latency, and KPI reliability. Re-usable patterns you can drop into teams without heavy ceremony. Reference architecture flowchart TD A["Ingest: Batch/Streaming"] --> B["Bronze Delta"] B --> C["Curate: Features"] C --> D["`ML Training MLflow tracking`"] D --> E["`Registry Stages: Staging/Prod`"] E --> F["Serving/Batch Scoring"] F --> G["`Monitoring Drift, KPI, Cost`"] Building blocks Delta Lake: schema evolution, Z-order, OPTIMIZE + VACUUM policies. MLflow: experiment tracking, model registry, stage transitions with approvals. CI/CD: notebooks/jobs packaged via repo; tests for data contracts and model code. Observability: input DQ, feature coverage, drift monitors, KPI windows, cost budgets. Sample: register and deploy import mlflow from mlflow.tracking import MlflowClient run_id = mlflow.active_run().info.run_id mlflow.sklearn.log_model(model, "model") client = MlflowClient() model_uri = f"runs:/{run_id}/model" client.create_registered_model("churn_model") client.create_model_version("churn_model", model_uri, run_id) client.transition_model_version_stage("churn_model", 1, stage="Staging") Guardrails Promotion requires DQ + performance gates; auto-revert on KPI regression. Cost envelopes by job cluster policy; latency SLOs per endpoint. Takeaways Ship small, measurable increments; automate checks; keep lineage and docs close to the code.