Déploiement

Model Drift

Définition

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.

Termes Connexes

Data Drift

A change in the statistical distribution of input data fed to a deployed model, which can degrade prediction quality even if the model itself has not changed. Detecting data drift early is critical for maintaining reliable production AI systems.

Post-Market Monitoring

The ongoing collection and analysis of data about a deployed AI system's real-world performance and risk profile, required for high-risk AI systems under the EU AI Act. Providers must proactively detect and report incidents, model drift, and new risks throughout the product's lifecycle.

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.

Services Connexes

Eu Ai Act Compliance

Product Leadership Program

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