Anomaly Detection
Also called: Outlier Detection
The process of identifying unusual patterns, behaviors, or events that deviate from expected system behavior and may indicate failures, risks, or performance issues.
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Related terms
The process of identifying significant changes in data distributions, model behavior, or prediction patterns that may indicate degraded AI system performance.
Failure AnalysisThe process of investigating errors, failures, or unexpected behavior in an AI system to identify root causes, assess their impact, and implement corrective or preventive actions.
AlertingThe process of automatically notifying users or systems when predefined conditions, thresholds, or anomalies indicate potential issues requiring attention.
Error TrackingThe process of capturing, recording, aggregating, and monitoring errors and exceptions generated by an AI system to support debugging, reliability, and operational visibility.
Model MonitoringThe practice of continuously tracking an AI model's performance, behavior, usage, and operational health in production.