- 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
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Detecting anomalous locations in geographic data
- Performing population analysis
- Altering data in your datafeed with runtime fields
- Adding custom URLs to machine learning results
- Handling delayed data
- Mapping anomalies by location
- Exporting and importing machine learning jobs
- 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.
Configure anomaly detection
editConfigure anomaly detection
editBefore you can use the Elastic Stack machine learning features, there are some configuration requirements (such as security privileges) that must be addressed. Refer to Setup and security.
To use the machine learning features to analyze your data, you can create an anomaly detection job and send your data to that job.
The results of machine learning analysis are stored in Elasticsearch and you can use Kibana to help you visualize and explore the results.
After you learn how to create and stop anomaly detection jobs, you can check the Examples for more advanced settings and scenarios.
Consult Working with anomaly detection at scale to learn more about the particularities of large anomaly detection jobs.
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