CI/CD for ML

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

End-to-end process for model training.

Model execution path in production.

Running predictions on large datasets periodically.

A mismatch between training and deployment data distributions that can degrade model performance.

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.