Why do TensorFlow models sometimes pass validation but fail in A/B testing?

Validation data is static and biased, while A/B testing reflects real-world dynamic user behavior.

Validation datasets are often curated, balanced, and temporally static, whereas A/B testing captures live user behavior influenced by seasonality, feedback loops, and system interactions. TensorFlow models optimized for offline metrics may overfit to validation distributions and fail when exposed to real-world variability.