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Model monitoring refers to the continuous observation of machine learning models in production to detect performance degradation, data and concept drift, and faulty predictions early. It includes metrics, alerting, explainability checks and retraining triggers, plus processes for root‑cause analysis and governance. The goal is reliable, maintainable model op…
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Model monitoring observes a deployed model and its environment to detect quality loss, unusual inputs, technical faults, or changing data early. It connects measurements to thresholds and responses.
It grew from production observability and statistical process control, then added data and concept drift for learning systems. Cloud services such as Vertex AI and tools such as Evidently operationalize this observation in MLOps pipelines.
Think of monitoring as a control loop: inputs and predictions are measured, compared with expectations or reference distributions, turned into alerts when they deviate, and followed by investigation, retraining, or rollback.
The distribution of incoming features changes relative to the data on which the model was built.
The relationship between inputs and the correct outcome shifts, making old patterns less reliable.
A defined boundary turns an observed deviation into an actionable operational signal.
Model monitoring reveals when a service that is technically running has become unreliable for its domain and needs investigation, calibration, or replacement.
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