This often happens due to time-travel or point-in-time issues.

During training, features must be retrieved as they existed at the prediction timestamp. If inference pulls the latest values instead, leakage or mismatches occur.

Ensure your feature store supports point-in-time correctness and that both training and inference use the same retrieval logic.

Also verify that feature freshness constraints are consistent.

Common mistakes include: Using latest features for historical training, Ignoring timestamp alignment, Mixing batch and real-time sources

The takeaway is that feature correctness is temporal, not just structural.