How do TensorFlow systems ensure safe rollout of new models in production?

They use staged deployment strategies like canary releases, shadow testing, and rollback mechanisms.

Safe rollout strategies involve incremental traffic shifting from old to new models. TensorFlow Serving supports versioned models, allowing traffic splitting. Monitoring systems track latency, error rate, and business KPIs. If anomalies are detected, automated rollback restores the previous stable model version.