Déploiement

Online Learning

Définition

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.

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.

Batch Inference

Running a model on a large dataset in a single scheduled job rather than in real time. Batch inference is more cost-efficient than real-time serving for use cases such as nightly report generation, bulk document classification, or periodic customer scoring.

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.

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