Monitoring

End-to-end process for model training.

Model execution path in production.

Running predictions on large datasets periodically.

Low-latency prediction per request.

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.

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.