Déploiement

Model Registry

Définition

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.

Termes Connexes

MLOps

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.

Model Serving

The process of deploying trained ML models to production environments where they can receive inputs and return predictions at scale. Model serving infrastructure must address throughput, latency, versioning, and cost while meeting SLAs.

Model Card

A short document accompanying a trained AI model that describes its intended uses, performance across demographic groups, limitations, and ethical considerations. Model cards promote transparency and are considered best practice under frameworks like the EU AI Act and ISO 42001.

Services Connexes

Pilot To Production

Eu Ai Act Compliance

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