Training Pipeline

Model execution path in production.

Running predictions on large datasets periodically.

Low-latency prediction per request.

Running new model alongside production without user impact.

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.

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