- Machine Learning: other versions:
- Setup and security
- Getting started with machine learning
- Anomaly detection
- Overview
- Concepts
- Configure anomaly detection
- API quick reference
- Supplied configurations
- Function reference
- Examples
- Configuring anomaly detection alerts
- Aggregating data for faster performance
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Detecting anomalous locations in geographic data
- Performing population analysis
- Transforming data with script fields
- Adding custom URLs to machine learning results
- Handling delayed data
- Limitations
- Troubleshooting
- Data frame analytics
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Supplied anomaly detection configurations
editSupplied anomaly detection configurations
editAnomaly detection jobs contain the configuration information and metadata necessary to perform an analytics task. Kibana can recognize certain types of data and provide specialized wizards for that context. This page lists the categories of the anomaly detection jobs that are ready to use via Kibana in Machine learning. Refer to Create anomaly detection jobs to learn more about creating a job by using supplied configurations. Logs and Metrics supplied configurations are available and can be created via the related solution UI in Kibana.
The configurations are only available if data exists that matches the queries specified in the manifest files. These recognizer queries are linked in the descriptions of the individual configurations.
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