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 provide ways to monitor deployed ML models for data drift, prediction quality, and operational health. AWS and GCP offer dedicated managed “model monitoring” features (SageMaker Model Monitor, Vertex AI Model Monitoring). Azure ML provides monitoring capabilities through Azure ML plus Azure Monitor/Application Insights for metrics, logs, and alerts. OCI Data Science relies on deployment telemetry and integrations with OCI Monitoring/Logging to build comparable monitoring and alerting.