The difference between a model and a product

A model predicts. A product survives failures, bad inputs, and changing business constraints. That gap is MLOps.

My deployment checklist

1) Data contract

• Input schema versioned
• Null/invalid behavior defined
• Backfill strategy documented

2) Training reproducibility

• Environment pinned
• Random seeds fixed
• Artifacts versioned

3) CI/CD gates

• Unit tests for feature transforms
• Integration tests for inference endpoints
• Quality thresholds enforced before deploy

4) Registry + rollout strategy

• Model version in registry
• Canary rollout with health checks
• One-command rollback path

5) Monitoring in production

• Latency, error rate, throughput
• Prediction distribution drift
• Data quality anomalies

If one of these is missing, you are not production-ready yet.

Related case studies

• AI Product Photo Detector — end-to-end MLOps with CI/CD, registry, drift checks, and monitoring.
• DAISI enterprise assistant — observability and scheduled evaluation in a production GenAI context.