training serving skew

This is two questions hiding as one: how would you have caught it before full rollout, and how do you debug it now. Answer both and you signal senior judgment. Here is the staged-rollout and root-cause playbook.

Online models need features that are fresh and computed exactly the way training computed them. The classic failure is training-serving skew that silently degrades the model. The signal is the online/offline split with one shared definition. Here is the design.

A feature store is easy to define and easy to get wrong. The signal is explaining the exact bug it prevents (training-serving skew) and the point-in-time correctness that stops label leakage. Here is the full picture.

Same model, two answers: clean offline, ugly online. The culprit is almost always a feature computed differently in the two paths. Here is the diff-based hunt that localizes it to a single column.