- 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.
Machine Learning Jobs
editMachine Learning Jobs
editMachine learning jobs contain the configuration information and metadata necessary to perform an analytics task.
Each job has one or more detectors. A detector applies an analytical function to specific fields in your data. For more information about the types of analysis you can perform, see Function Reference.
A job can also contain properties that affect which types of entities or events are considered anomalous. For example, you can specify whether entities are analyzed relative to their own previous behavior or relative to other entities in a population. There are also multiple options for splitting the data into categories and partitions. Some of these more advanced job configurations are described in the following section: Configuring Machine Learning.
For a description of all the job properties, see Job Resources.
In Kibana, there are wizards that help you create specific types of jobs, such as single metric, multi-metric, and population jobs. A single metric job is just a job with a single detector and limited job properties. To have access to all of the job properties in Kibana, you must choose the advanced job wizard. If you want to try creating single and multi-metrics jobs in Kibana with sample data, see Getting Started.
You can also optionally assign jobs to one or more job groups. You can use job groups to view the results from multiple jobs more easily and to expedite administrative tasks by opening or closing multiple jobs at once.