Data Drift
Also called: Dataset Drift, Input Drift
A change in the statistical distribution or characteristics of input data over time that can reduce the performance of an AI model.
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Related terms
A change in the relationship between inputs and expected outputs over time, causing a model's predictions to become less accurate.
Drift DetectionThe process of identifying significant changes in data distributions, model behavior, or prediction patterns that may indicate degraded AI system performance.
Model MonitoringThe practice of continuously tracking an AI model's performance, behavior, usage, and operational health in production.
Anomaly DetectionThe process of identifying unusual patterns, behaviors, or events that deviate from expected system behavior and may indicate failures, risks, or performance issues.
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.