- X-Pack Reference for 6.0-6.2 and 5.x:
- Introduction
- Setting Up X-Pack
- Breaking Changes
- X-Pack APIs
- Graphing Connections in Your Data
- Profiling your Queries and Aggregations
- Reporting from Kibana
- Securing the Elastic Stack
- Getting Started with Security
- How Security Works
- Setting Up User Authentication
- Configuring SAML Single-Sign-On on the Elastic Stack
- Configuring Role-based Access Control
- Auditing Security Events
- Encrypting Communications
- Restricting Connections with IP Filtering
- Cross Cluster Search, Tribe, Clients and Integrations
- Reference
- Monitoring the Elastic Stack
- Alerting on Cluster and Index Events
- Machine Learning in the Elastic Stack
- Troubleshooting
- Getting Help
- X-Pack security
- Can’t log in after upgrading to 6.2.4
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- X-Pack Watcher
- X-Pack monitoring
- X-Pack machine learning
- Limitations
- License Management
- Release Notes
WARNING: Version 6.2 of the Elastic Stack has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Configuring Machine Learning
editConfiguring Machine Learning
editIf you want to use X-Pack machine learning features, there must be at least one machine learning node in your cluster and all master-eligible nodes must have machine learning enabled. By default, when you install X-Pack, all nodes are machine learning nodes. For more information about these settings, see X-Pack Settings.
To use the X-Pack machine learning features to analyze your data, you must create a job and send your data to that job.
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If your data is stored in Elasticsearch:
- You can create a datafeed, which retrieves data from Elasticsearch for analysis.
- You can use Kibana to expedite the creation of jobs and datafeeds.
- If your data is not stored in Elasticsearch, you can POST data from any source directly to an API.
The results of machine learning analysis are stored in Elasticsearch and you can use Kibana to help you visualize and explore the results.
For a tutorial that walks you through these configuration steps, see Getting Started.
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 scenarios in this section describe some best practices for generating useful machine learning results and insights from your data.