The Ultimate Databricks MLOps Production Blueprint

Don't rely on memory. Rely on a system.

Moving a Machine Learning model from a notebook to a production environment is complex. It involves dozens of small but critical steps—from configuring Unity Catalog permissions to enabling autoscaling and drift monitoring.

Missing just one step can lead to security vulnerabilities, cloud cost spikes, or silent model failures.

The Databricks MLOps Production Checklist is your safety net.

It is a comprehensive Standard Operating Procedure (SOP) designed specifically for the Databricks ecosystem (Delta Lake, MLflow, Unity Catalog). Use it to audit your own work or review your team's pull requests before deployment.

✅ What’s Inside?

A structured, 6-phase audit covering the entire lifecycle:

• Phase 1: Data Preparation (Medallion Architecture & Security)
• Phase 2: Feature Engineering (Feature Store & Skew prevention)
• Phase 3: Training (Experiment Tracking & Reproducibility)
• Phase 4: Validation (Metrics, Compliance & Governance)
• Phase 5: Deployment (CI/CD, Model Serving, Champion/Challenger)
• Phase 6: Monitoring (Drift Detection & Retraining)

Who is this for?

• ML Engineers who want a quick "pre-flight" check before deployment.
• Data Scientists moving into engineering roles who need to know what "production-ready" looks like.
• Team Leads who want to standardize their team's delivery process.

About the Author

Daniel García is a Full Stack Machine Learning Engineer and PhD Candidate specializing in Computer Vision and MLOps. He focuses on bridging the gap between academic research and industrial application.

• LinkedIn: iamdgarcia
• GitHub: iamdgarcia

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