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Concept for observing AI/ML systems in production, combining metrics, logs and model signals to track performance, drift and fairness.
AI Observability describes practices for monitoring, diagnosing and explaining AI/ML systems in production. It combines metrics, logs, model signals and data‑drift analysis to understand performance, fairness and robustness. The goal is early detection, root‑cause analysis and continuous improvement. Practices include metric design, monitoring pipelines and diagnostic tools.
Measures prediction quality against available labels.
Compares current feature distributions to reference.
Time between request and prediction, important for SLAs.
Implementation of a drift detector that identifies distribution shifts and triggers retraining.
Dashboard showing segment metrics and historical bias trends to support decisions.
Automated alerts with a playbook for on‑call and incident response for model failures.
Define relevant metrics and SLAs
Build telemetry pipelines and storage
Set up dashboards, alerts and playbooks