IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Anomaly detection examples
editAnomaly detection examples
editThough it is quite simple to analyze your data and provide quick machine learning results, gaining deep insights might require some additional planning and configuration. The scenarios in this section describe some best practices for generating useful machine learning results and insights from your data.
- Adding custom URLs to machine learning results
- Aggregating data for faster performance
- Altering data in your datafeed with runtime fields
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Detecting anomalous locations in geographic data
- Generating alerts for anomaly detection jobs
- Handling delayed data
- Mapping anomalies by location
- Performing population analysis
- Exporting and importing machine learning jobs
Anomaly detection examples in blog posts
editThe blog posts listed below show how to get the most out of Elastic machine learning anomaly detection.
- Sizing for machine learning with Elasticsearch
- Filtering input data to refine machine learning jobs
- Temporal vs. population analysis in Elastic machine learning
- Using Elasticsearch and machine learning for IT Operations
- Using machine learning and Elasticsearch for security analytics
- User annotations for Elastic machine learning
- Custom Elasticsearch aggregations for machine learning jobs
- Analysing Linux auditd anomalies with Auditbeat and machine learning
- How to optimize Elasticsearch machine learning job configurations using job validation
- Interpretability in machine learning: Identifying anomalies, influencers, and root causes