Amazon Prometheus Adds Anomaly Detection

Amazon Managed Service for Prometheus, a fully managed Prometheus-compatible monitoring service now supports anomaly detection. Anomaly detection applies machine-learning algorithms to continuously analyze time series and surfaces anomalies with minimal user intervention. You can use anomaly detection to isolate and troubleshoot unexpected changes in your metric behavior.

Amazon Managed Service for Prometheus Anomaly Detection currently supports Random Cut Forest (RCF), an unsupervised algorithm for detecting anomalous data points within a time series. Once you create and configure an anomaly detector in an Amazon Managed Service for Prometheus workspace, it will create four new time series to represent resulting anomalies and confidence values along with them. Based on the resulting time series, you can create dynamic alerting rules in the Amazon Managed Service for Prometheus Alert manager, to notify you when anomalies occur, and you can also visualize the resulting time series alongside the input time series either in self-managed Grafana or Amazon Managed Grafana dashboards.

This feature is now available in all AWS regions where Amazon Managed Service for Prometheus is generally available. To configure anomaly detection use the AWS CLI, SDK, or APIs. Check out the Amazon Managed Service for Prometheus user guide for detailed documentation.

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Source: Amazon Web Services