What is a shadow deployment for ML models and when is it useful?

A shadow deployment runs a new model in production alongside the current model, but its predictions don’t affect users. It’s useful to validate latency, stability, and distribution differences on real traffic before a canary or full rollout. Compare predictions, monitor drift, and catch integration bugs safely.