Failure Analysis
Also called: Root Cause Analysis
The 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.
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Related terms
The process of capturing, recording, aggregating, and monitoring errors and exceptions generated by an AI system to support debugging, reliability, and operational visibility.
Drift DetectionThe process of identifying significant changes in data distributions, model behavior, or prediction patterns that may indicate degraded AI system performance.
Distributed TracingAn observability technique that tracks requests as they flow across multiple distributed services to measure latency, diagnose failures, and analyze system behavior.
LoggingThe practice of recording application, model, and system events to support debugging, monitoring, troubleshooting, and operational analysis.
AI ObservabilityThe practice of monitoring, tracing, and analyzing AI systems to understand their behavior, performance, reliability, and operational health in production.