النشر

A/B Testing (ML)

التعريف

A controlled experiment that compares the performance of two or more model versions by routing live traffic between them and measuring business and technical metrics. A/B testing is essential for validating that a new model version improves outcomes before full rollout.

مصطلحات ذات صلة

Canary Deployment

A progressive rollout strategy that routes a small percentage of production traffic to a new model version while the remainder continues to receive the old version. Canary deployments limit blast radius if the new model underperforms or causes regressions.

Shadow Mode

A deployment technique where a new model processes production traffic in parallel with the existing system but its predictions are logged rather than served to users. Shadow mode enables risk-free validation of model behaviour on real-world data before cutover.

Model Drift

The degradation of a deployed model's performance over time as the statistical properties of production data diverge from its training distribution. Model drift monitoring is a key MLOps discipline and is implicitly required by the EU AI Act's post-market monitoring obligations.

خدمات ذات صلة

Product Leadership Program

تحتاج مساعدة في فهم الذكاء الاصطناعي؟

احجز مكالمة تقييم ملاءمة Physical AI لمناقشة كيفية تطبيق مفاهيم الذكاء الاصطناعي هذه على قطاعك وتحدياتك.