アーキテクチャ

MLOps

定義

The practice of combining Machine Learning, DevOps, and data engineering to streamline the deployment, monitoring, and maintenance of ML models in production. MLOps ensures reliable, scalable, and reproducible ML systems across their entire lifecycle.

関連用語

Model Registry

A centralised repository for versioning, cataloguing, and managing trained ML models throughout their lifecycle—from experimentation through staging to production. A model registry is the foundation of reproducible, auditable AI deployments.

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.

Feature Store

A centralised platform for creating, storing, sharing, and serving ML features consistently across training and inference pipelines. Feature stores eliminate training-serving skew and accelerate model development by making pre-computed features reusable across teams.

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MLOps

定義

関連用語

Model Registry

Model Drift

Feature Store

関連サービス

AIの理解にお困りですか？

MLOps

定義

関連用語

Model Registry

Model Drift

Feature Store

関連サービス

AIの理解にお困りですか？