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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

Feature Store

A system providing point-in-time correct training-set generation and a low-latency online store serving identical values.

Point-in-Time Correctness

Building each training row from only the feature values that existed at that row's timestamp.

This term is part of the free AI/ML Engineer interview preparation module - browse the full glossary for every definition.