- 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
editAPI 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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