AI observability is the practice of monitoring artificial intelligence and machine learning systems in production, so teams can see how models behave, catch degradation early, and understand why outputs change over time. It brings model performance metrics, data monitoring, and system telemetry into a single view.
Typical signals include prediction accuracy, latency, input data drift, and shifts in output distribution. For example, a fraud model might be watched for a sudden change in score distribution after an upstream data change, flagging trouble before losses appear.
AI observability shows that a model’s behavior shifted. Pinning down the cause usually means reproducing the exact data state a run used, so detection and reproducibility work as separate but complementary steps.