AI Observability
Also called: AI System Observability
The practice of monitoring, tracing, and analyzing AI systems to understand their behavior, performance, reliability, and operational health in production.
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Related terms
The practice of continuously tracking an AI model's performance, behavior, usage, and operational health in production.
Application MonitoringThe practice of collecting and analyzing telemetry from applications to track performance, availability, errors, and overall operational health.
LoggingThe practice of recording application, model, and system events to support debugging, monitoring, troubleshooting, and operational analysis.
Distributed TracingAn observability technique that tracks requests as they flow across multiple distributed services to measure latency, diagnose failures, and analyze system behavior.
Metrics CollectionThe process of gathering quantitative measurements from AI systems and applications to track performance, reliability, usage, and operational health.