Continuously tracking AI model performance, data quality, and system health in production to detect issues early. Like having health checkups to catch problems before they become serious.
Model monitoring alerts the team when prediction accuracy drops below 95% or when incoming data looks different from training data.
All major clouds support monitoring deployed ML models for data drift, prediction quality, and operational health. AWS and GCP provide dedicated managed model monitoring features (SageMaker Model Monitor, Vertex AI Model Monitoring). Azure ML supports monitoring through Azure ML capabilities integrated with Azure Monitor/Application Insights and logging/metrics pipelines. OCI commonly uses OCI Monitoring/Logging with custom metrics and telemetry from model endpoints; some monitoring is implemented by combining OCI services rather than a single named feature.