- 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.
API quick reference
edit
IMPORTANT: This documentation is no longer updated. Refer to Elastic's version policy and the latest documentation.
API quick reference
editAll machine learning anomaly detection endpoints have the following base:
/_ml/
The main resources can be accessed with a variety of endpoints:
-
/anomaly_detectors/: Create and manage anomaly detection jobs -
/calendars/: Create and manage calendars and scheduled events -
/datafeeds/: Select data from Elasticsearch to be analyzed -
/filters/: Create and manage filters for custom rules -
/results/: Access the results of an anomaly detection job -
/model_snapshots/: Manage model snapshots
For a full list, see Machine learning anomaly detection APIs.
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