Observability
The capability to infer an AI system’s internal state and behavior through collection and analysis of logs, metrics, and outputs for effective monitoring and troubleshooting.
Definition
Goes beyond basic monitoring to provide deep insights into system health. Observability pipelines collect structured logs (requests, errors), metrics (latency, resource use), and traces (execution paths) from data-ingestion, training, and inference services. With correlations and dashboards, teams can pinpoint root causes of issues, replay events, and perform post-mortems. Governance defines which signals to capture, retention policies, and alert thresholds to maintain system reliability and compliance.
Real-World Example
A fraud-detection platform integrates OpenTelemetry to emit traces for every transaction, logs model decisions with confidence scores, and tracks CPU/GPU usage. When latency spikes, the SRE team drills into traces to discover a slow feature-store query, fixes the indexing, and restores normal performance within 15 minutes.
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