#A/B Testing

Articles about A/B Testing — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with a/b testing. ← All posts

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Pratik Dhanave · ·6 min read

Evaluating Recommenders

A recommender that scores well offline can flop in production, and a metric that looks like success can quietly harm the product. Evaluating recommenders is genuinely hard: offline metrics only approximate real behavior, the only ground truth is a live A/B test, and the very act of recommending shapes the data you learn from next. This post covers offline metrics, online testing, the gap between them, and the feedback loops that make evaluation a moving target.

A recommender that scores well offline can flop in production, and a metric that looks like success can quietly harm the product. Evaluation is genuinely hard: offline metrics only approximate real behavior, the only ground truth is a live A/B test, and the very act of recommending shapes the data you learn from next. Offline metrics, online testing, the offline-online gap, and feedback loops.

#Recommender Systems#Evaluation#A/B Testing#Machine Learning#Personalization#Testing

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.