MLOps

Shadow Deployment

Running a new model on real traffic without serving its output, comparing scores and latency against the incumbent.

Why interviewers ask about it

Answers 'does it break?' at zero user risk - including latency regressions offline evaluation cannot see. Only a powered A/B answers 'is it better?'.

Ramping a new version through 1% → 5% → 25% → 100% with automated guardrail thresholds.

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