Online Inference

Running new model alongside production without user impact.

Incrementally deploying new models to reduce risk.

Maintaining two environments for instant rollback.

Centralized repository for curated features.

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.