Create anomaly detector

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Davis Anomaly Detection Davis Anomaly Detection allows you to create anomaly detectors, set up customized alerts, and transform metric events configuration. You can also save time and create an anomaly detector in Notebooks Notebooks while using the app.

Prerequisites

To use the latest version of Davis Anomaly Detection, you need to have appropriate permissions. For more information, see Davis Anomaly Detection app overview.

Create an anomaly detector

To manually create an Anomaly Detection configuration

  1. Go to Davis Anomaly Detection Davis Anomaly Detection - new.
  2. Select Add Anomaly Detector > Create your own Anomaly Detector.
  3. Give your configuration a meaningful Title.
  4. Expand Configure your query and provide the DQL query to fetch your data.

    We recommend that you use the interval: 1m parameter to ensure proper data resolution for the analysis.

  5. Expand Customize parameters and define the analyzer and its parameters. For details, see Analyzer type and parameters.
  6. Expand Create an event template and configure the event triggered by the configuration. For details, see Event template.
  7. Select Create.

    Check the Status of the new configuration shortly after creation to ensure there are no errors in the execution.

Create an anomaly detector in Notebooks

With Davis for Notebooks, you can preview your Anomaly Detection configuration and evaluate its effectiveness. This option takes you to Notebooks Notebooks, where you configure the query and monitoring strategy, and then back to Davis Anomaly Detection Davis Anomaly Detection to create an event template.

  1. Go to Davis Anomaly Detection Davis Anomaly Detection - new.
  2. Select Add Anomaly Detector > Anomaly Detector in Notebooks.
  3. Select a notebook in which you want to preview your configuration.
    This action takes you to Notebooks.
  4. Add a new Query Grail or Metrics section and query the data you're interested in.

    For a DQL query, we recommend that you use the interval: 1m parameter to ensure proper data resolution for the analysis.

  5. Select Options > Davis AI.
  6. Activate the analyzer.
  7. Select the required analyzer and configure it. For details, see Anomaly detection configuration.
  8. Select Run analysis.
  9. Once you're satisfied with the result, select More actions > Open with Open with and select Davis Anomaly Detection.
    This action takes you back to Davis Anomaly Detection Davis Anomaly Detection.
  10. Expand Create an event template and configure the event triggered by the configuration. For details, see Event template.
  11. Select Create.

    Check the Status of the new configuration shortly after creation to ensure there are no errors in the execution.

Transform a metric event configuration

Metric events enhance anomaly detection, expanding them beyond out-of-the-box use cases into metric-based events. With the power of DQL, you can extend this reach further.

To convert a metric event to the Anomaly Detection configuration

  1. Go to Davis Anomaly Detection Davis Anomaly Detection - new.
  2. Select Add Anomaly Detector > Improve metric events with DQL.
  3. Select the required metric event and select Transform.
  4. To configure an auto-adaptive threshold and seasonal baseline, adapt the query to a 1-minute resolution.
    1. For the newly created configuration, select More actions > Edit.
    2. Expand the Configure your query section.
    3. Append the interval:1m parameter to the query.
    4. Save your changes.
  5. The converted metric event is automatically disabled, and the newly created configuration is active instead.

    Check the Status of the new configuration shortly after creation to ensure there are no errors in the execution.

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