How-tos
Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud. Availability This functionality is generally available and ready for production usage. Serverless interfaces and procedures might differ from classic Elastic Stack deployments. The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats. Availability This functionality is generally available and ready for production usage. Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments. This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.
Serverless
Generally available
Stack
Generally available
Though 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 guides in this section describe some best practices for generating useful machine learning results and insights from your data.
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Using runtime fields in datafeeds
- 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
The 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
There are also some examples in the machine learning folder in the examples repository.