- 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
Configure anomaly detection
editConfigure anomaly detection
editIf you want to use machine learning features, there must be at least one machine learning node in your cluster and all master-eligible nodes must have machine learning enabled. By default, all nodes are machine learning nodes. For more information about these settings, see machine learning nodes.
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.