How do TensorFlow production systems detect model regressions before user-facing impact occurs?

They use shadow deployments, canary testing, and statistical monitoring of prediction distributions.

Model regression detection in TensorFlow systems happens before full rollout using shadow traffic (duplicate inference without serving output), canary deployments (small traffic percentage), and statistical monitoring of outputs like entropy, confidence distributions, and feature drift. These systems compare new model behavior against a baseline to detect degradation early without relying on ground truth labels.