デプロイメント

A/B Testing (ML)

定義

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.

関連用語

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.

AIの理解にお困りですか？

Physical AI 適合性コールをご予約いただき、これらのAI概念が貴社の業界や課題にどう適用されるかをご相談ください。