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Offline vs Online Evaluation

Offline evaluation scores a change against a fixed golden set: fast, cheap, repeatable, and runnable in CI before anything ships. Online evaluation measures the change on real traffic and real users, usually via A/B, and is the only true read on impact. The two are not interchangeable: offline gains routinely fail to hold online because of distribution shift and metric gaming. The discipline FDE loops test is using offline to gate and online to confirm.

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RELATED CONCEPTS

Golden Datasets and Eval Sets A/B, Canary and Shadow Testing Evaluating RAG Systems LLM-as-a-Judge

LESSONS THAT TEACH THIS

The FDE EngagementAn evaluation becomes a harness the day it can block a release24 min→The FDE EngagementMeasure the baseline and distinguish time, capacity and cash28 min→Hard DeploymentsTwenty deployments need one harness and twenty sets of cases30 min→

PRACTICE THIS IN REAL QUESTIONS

Machine Learning & Data ScienceExplain precision, recall and F1, and which one matters for a fraud-detection customer?→Machine Learning & Data ScienceYour model has 0.95 ROC AUC. The customer is unimpressed in production. What does AUC actually tell you?→ML System Design (Product)Design an evaluation framework for an ads-ranking system.→Machine Learning & Data ScienceThe customer's dataset is 99.5% negatives. Their model shows 99.5% accuracy. Walk me through what you'd do.→Machine Learning & Data ScienceYour model scored 95% in the pilot and 70% in production. What happened?→Machine Learning & Data ScienceExplain k-fold cross-validation, and when would you refuse to use it?→