View recommended anomaly detectors - Amazon OpenSearch Service
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View recommended anomaly detectors

Anomaly detection in Amazon OpenSearch Service automatically detects anomalies in your OpenSearch data in near-real time by using the Random Cut Forest (RCF) algorithm. RCF is an unsupervised machine learning algorithm that models a sketch of your incoming data stream. The algorithm computes an anomaly grade and confidence score value for each incoming data point. Anomaly detection uses these values to differentiate an anomaly from normal variations in your data.

To simplify the process of creating anomaly detectors, Amazon Q can generate suggested detectors based on your selected data source on the Discover page. Amazon Q supports suggested anomaly detectors for any language.

To view Amazon Q recommended anomaly detectors

  1. Verify that you've set up Amazon Q for OpenSearch Service.

  2. In the OpenSearch Dashboards main menu, choose the Discover page, and then choose a data source.

  3. From the Amazon Q menu, choose Suggest anomaly detector, as shown in the following screen shot.

    An image showing how to locate the button to create a suggested anomaly detector.

    Amazon Q can take a few seconds to generate the features for the detector.

  4. Choose Create detector.