Business

Production AI

Définition

AI systems that are live in enterprise environments, processing real transactions and influencing business decisions at scale. Production AI requires robust MLOps practices, monitoring, compliance controls, and SLAs—a significantly higher bar than prototype or pilot systems.

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 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.

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.

Services Connexes

Product Leadership Program

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