Déploiement

Data Drift

Définition

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.

Termes Connexes

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.

Online Learning

A training paradigm where a model is continuously updated with new data as it arrives, rather than in discrete batch retraining cycles. Online learning is used in recommendation systems and fraud detection where data distributions shift rapidly.

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