- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Tutorial: Getting started with anomaly detection
- Advanced concepts
- API quick reference
- How-tos
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Altering data in your datafeed with runtime fields
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Performing population analysis
- Reverting to a model snapshot
- Detecting anomalous locations in geographic data
- Mapping anomalies by location
- Adding custom URLs to machine learning results
- Anomaly detection jobs from visualizations
- Exporting and importing machine learning jobs
- Resources
- Data frame analytics
- Natural language processing
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Appendix F: Metrics anomaly detection configurations
editAppendix F: Metrics anomaly detection configurations
editThese anomaly detection jobs can be created in the Infrastructure app in Kibana. For more information about their usage, refer to Inspect metric anomalies.
Metrics hosts
editDetect anomalous memory and network behavior on hosts.
Name | Description | Job | Datafeed |
---|---|---|---|
hosts_memory_usage |
Identify unusual spikes in memory usage across hosts. |
||
hosts_network_in |
Identify unusual spikes in inbound traffic across hosts. |
||
hosts_network_out |
Identify unusual spikes in outbound traffic across hosts. |
Metrics Kubernetes
editDetect anomalous memory and network behavior on Kubernetes pods.
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