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Getting started with machine learning »
Elastic Docs ›Machine Learning in the Elastic Stack [7.9]

Set up machine learning features

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Set up machine learning features

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To use the Elastic Stack machine learning features, you must have the appropriate subscription and at least one machine learning node in your cluster.

In Kibana, the machine learning features must be visible in your space and your source index patterns must exist in the same space as your machine learning jobs.

If Elastic Stack security features are enabled, you must also ensure your users have the necessary privileges.

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

Machine learning nodes

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To use machine learning features, there must be at least one machine learning node in your cluster. A machine learning node is a node that has xpack.ml.enabled set to true and ml in node.roles.

If nodes do not have the machine learning role, they cannot run machine learning jobs. If xpack.ml.enabled is true, however, they can service API requests. For more information, see Machine learning nodes and Machine learning settings in Elasticsearch.

Security privileges

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The Elastic Stack security features provide roles and privileges that make it easier to control which users can manage or view machine learning objects such as jobs, datafeeds, results, and model snapshots. Kibana also enables you to control access to the machine learning features within each space. For more information, see Security privileges and Kibana privileges.

For full access to the machine learning features in Kibana, you must have:

  • all Kibana privileges for the machine learning features in the appropriate spaces
  • read and view_index_metadata index privileges on source indices
  • read, manage, and index index privileges on destination indices (for data frame analytics jobs only)

For read-only access to the machine learning features in Kibana, you must have:

  • read Kibana privileges for the machine learning features in the appropriate spaces
  • read index privileges on source indices
  • read index privileges on destination indices (for data frame analytics jobs only)

To upload files in Kibana with the File Data Visualizer, you must have:

  • all Kibana privileges for the machine learning features in the appropriate spaces. Alternatively, read Kibana privileges for the machine learning features and all Kibana privileges for the index pattern management feature
  • ingest_admin built-in role
  • read, manage, and index index privileges for destination indices

You cannot limit access to specific machine learning objects in each space. If the machine learning feature is visible in your space and you have read or all Kibana privileges for the feature, you have access to all machine learning objects in that space.

If you call machine learning APIs directly, you must have the index privileges listed above as well as machine_learning_admin or machine_learning_user built-in roles.

The machine_learning_admin and machine_learning_user roles grant access to the machine learning features in all Kibana spaces. Therefore, when you use Kibana, use custom roles instead and set your Kibana privileges appropriately for each space.

Getting started with machine learning »