Shadow Deployment

Incrementally deploying new models to reduce risk.

Maintaining two environments for instant rollback.

Centralized repository for curated features.

Shift in feature distribution over time.

End-to-end process for model training.

Model execution path in production.

Running predictions on large datasets periodically.

Low-latency prediction per request.