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Elastic Docs ›Machine Learning in the Elastic Stack [7.16]
Getting started with machine learning »

Set up machine learning featuresedit

Requirements overviewedit

To use the Elastic Stack machine learning features, you must have:

  • the appropriate subscription level or the free trial period activated
  • xpack.ml.enabled set to its default value of true on every node in the cluster (refer to Machine learning settings in Elasticsearch)
  • ml value defined in the list of node.roles on the machine learning nodes
  • machine learning features visible in the Kibana space

  • security privileges assigned to the user that:

    • grant use of machine learning features, and
    • grant access to source and destination indices.

The fastest way to get started with machine learning features is to start a free 14-day trial of Elasticsearch Service in the cloud.

Security privilegesedit

Assigning security privileges affects how users access machine learning features. Consider the two main categories:

  • Elasticsearch API user: uses an Elasticsearch client, cURL, or Kibana Dev Tools to access machine learning features via Elasticsearch APIs. It requires Elasticsearch security privileges.
  • Kibana user: uses the machine learning features in Kibana and does not use Dev Tools. It requires either Kibana feature privileges or Elasticsearch security privileges and is granted the most permissive combination of both. Kibana feature privileges are recommended if you control job level visibility via Spaces. Machine learning features must be visible in the relevant space. Refer to Feature visibility in Spaces for configuration information.

You can configure these privileges under Stack Management > Security in Kibana or via the respective Elasticsearch security APIs.

Elasticsearch API useredit

If you use machine learning APIs, you must have the following cluster and index privileges:

For full access:

  • machine_learning_admin built-in role or the equivalent cluster privileges
  • read and view_index_metadata on source indices
  • read, manage, and index on destination indices (for data frame analytics jobs only)

For read-only access:

  • machine_learning_user built-in role or the equivalent cluster privileges
  • read index privileges on source indices
  • read index privileges on destination indices (for data frame analytics jobs only)

Kibana securityedit

Feature visibility in Spacesedit

In Kibana, the machine learning features must be visible in your space. To control which features are visible in your space, use Stack Management > Kibana > Spaces.

Manage spaces in Kibana

In addition to index privileges, source data views must also exist in the same space as your machine learning jobs. These can be configured in Stack Management > Kibana > Data Views.

Each machine learning job can be assigned to all, one or multiple spaces. This can be configured in Stack Management > Alerts and Insights > Machine Learning Jobs. To assign a job to a space, select the spaces icon shown in the job list.

Assign machine learning jobs to spaces

Kibana useredit

Within a Kibana space, for full access to the machine learning features, you must have:

  • Machine Learning: All Kibana privileges
  • Data Views Management: All Kibana feature privileges
  • read, and view_index_metadata index privileges on your source indices
  • data views for your source indices
  • data views, read, manage, and index index privileges on destination indices (for data frame analytics jobs only)

Within a Kibana space, for read-only access to the machine learning features, you must have:

  • Machine Learning: Read Kibana privileges
  • data views for your source indices
  • read index privilege on your source indices
  • data views and read index privileges on destination indices (for data frame analytics jobs only)

Data views can be automatically created when creating a data frame analytics job.

For access to use machine learning APIs via Dev Tools in Kibana, set the Elasticsearch security privileges and grant access to machine_learning_admin or machine_learning_user built-in roles.

Data Visualizer featureedit

Within a Kibana space, to upload and import files in the Data Visualizer, you must have:

  • Machine Learning: Read or Discover: All Kibana feature privileges
  • Data Views Management: All Kibana feature privileges
  • ingest_admin built-in role, or manage_ingest_pipelines cluster privilege
  • create, create_index, manage and read index privileges for destination indices

For more information, see Security privileges and Kibana privileges.

Getting started with machine learning »