A model can perform well offline and still drop in production, even when the weights are identical. The gap usually comes from how features are computed.

If training and serving use different logic, even small differences, the model ends up seeing different inputs in production than it saw during training.

This training-serving inconsistency is one of the most common silent failures in ML systems.

Feature stores exist to solve this. They provide shared feature definitions so the same logic is used in both training and serving, keeping inputs consistent and predictions reliable.