Core

A/B, Canary and Shadow Testing

Offline evals tell you a change is plausibly better; online testing tells you it actually is. A/B randomizes users between versions to measure real impact with statistical significance. Canary routes a small slice of live traffic to the new version to limit blast radius, and shadow mirrors real traffic to it with no user-facing effect so you can validate safely before anyone is exposed.

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RELATED CONCEPTS

Golden Datasets and Eval Sets Model Registry and Promotion Observability for AI Systems

PRACTICE THIS IN REAL QUESTIONS

ML System Design (Product)Design an evaluation framework for an ads-ranking system.→MLOps & ML EngineeringWhat's the difference between shadow deployment and A/B testing a model?→ML System Design (Product)Design a recommendation engine for an online-course (or e-commerce) catalog.→ML System Design (Product)Design the 'For You' ranking system for a short-video feed.→ML System Design (Product)Design learning-to-rank for product/marketplace search.→Machine Learning & Data ScienceExplain what a p-value actually is, to me, and then to a customer's VP of Marketing.→