Why does a Keras model show good validation metrics but fail in A/B testing?

Validation datasets often fail to represent real-world distribution, causing performance gaps in production A/B tests.

This happens due to dataset bias, temporal leakage, or overly clean validation splits. Offline metrics assume i.i.d. data, while A/B testing exposes real user behavior, noisy inputs, and distribution shift.