Anomaly detection is how AIOps spots trouble before a threshold is breached. This guide explains what it is and how it works in operations.
Anomaly detection is the use of machine learning to identify unusual patterns in operational data, metrics, logs, traces or events, that deviate from normal behaviour, often before a static threshold would fire. It lets AIOps catch emerging problems that fixed rules would miss.
Traditional monitoring alerts when a metric crosses a fixed threshold. But normal behaviour varies by time of day, season and workload, so static thresholds either miss real problems or drown teams in false alarms. Anomaly detection learns what normal looks like and flags genuine deviations, adapting as behaviour changes.
Anomaly detection models baseline behaviour from historical data and continuously compares live signals against it, accounting for seasonality and trends. When a metric, a pattern of logs or a distribution shifts beyond what is expected, it raises an anomaly, often earlier and with less noise than threshold alerts.
Detecting an anomaly is the start, not the end. On its own it is another alert. In autonomous operations, an anomaly triggers investigation and, where the cause is clear, governed remediation, so an unusual pattern becomes a resolved incident rather than a notification.
Ops Singularity uses anomaly detection as an input to the OIAO loop: anomalies across service, infrastructure, data and cost signals trigger investigation and governed resolution, rather than just alerting. Explore the platform.
It is used to catch unusual patterns in operational data, often before a static threshold would fire, so emerging problems are detected earlier and with less noise than fixed-rule alerting.
Static thresholds alert when a metric crosses a fixed value. Anomaly detection learns normal behaviour, including seasonality and trends, and flags genuine deviations, reducing both missed incidents and false alarms.
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