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.