Infrastructure

Model Monitoring

Continuous tracking of ML model performance in production, including accuracy metrics, data drift detection, and operational health. Essential for maintaining model quality over time.

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MLOps
Operational practices for machine-learning systems — data pipelines, model training pipelines, feature stores, drift monitoring, retraining. Precursor discipline to LLMOps; still relevant for traditional ML systems that run alongside GenAI.

Also referenced: Model Drift, Observability.

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Model Monitoring is part of the Infrastructure vocabulary used in the Generative AI Maturity Framework. See the full glossary for the complete set of 149 defined terms, or take the free maturity assessment to see where your organisation stands.

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Where this fits