Batch Inference

Low-latency prediction per request.

Running new model alongside production without user impact.

Incrementally deploying new models to reduce risk.

Maintaining two environments for instant rollback.

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.

End-to-end process for model training.

Model execution path in production.