Architecture

MLOps

Définition

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.

Termes Connexes

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.

Services Connexes

Product Leadership Program

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