MLOps
Training/Serving Skew
Any difference between how a feature is computed at training time and at inference time.
Why interviewers ask about it
Detect by logging serving features and re-scoring them offline with the training pipeline. That one practice catches implementation, freshness and source skew simultaneously.
Related terms
This term is part of the free AI/ML Engineer interview preparation module - browse the full glossary for every definition.