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. If the operator privileges feature is enabled, there are some machine learning settings that can be updated only by operator users.
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 nodesedit
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
If nodes do not have the machine learning role, they cannot run machine learning jobs. If
true, however, they can service API requests. For more
information, see Machine learning nodes and
Machine learning settings in Elasticsearch.
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. You can manage your roles, privileges, and spaces in the Stack Management app in Kibana. For more information, see Security privileges and Kibana privileges.
For full access to the machine learning features in Kibana, you must have:
allKibana privileges for the machine learning features in the appropriate spaces
view_index_metadataindex privileges on source indices
indexindex privileges on destination indices (for data frame analytics jobs only)
For read-only access to the machine learning features in Kibana, you must have:
readKibana privileges for the machine learning features in the appropriate spaces
readindex privileges on source indices
readindex privileges on destination indices (for data frame analytics jobs only)
To upload files in Kibana with the File Data Visualizer, you must have:
allKibana privileges for the machine learning features in the appropriate spaces. Alternatively,
readKibana privileges for the machine learning features and
allKibana privileges for the index pattern management feature
indexindex 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
privileges for the feature, you have access to all machine learning objects in that space.
If you do not use Kibana and instead call machine learning APIs directly, you must have the
index privileges listed above as well as
machine_learning_user built-in roles.
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