Retrieves configuration information for a trained inference model.
This functionality is experimental and may be changed or removed completely in a future release. Elastic will take a best effort approach to fix any issues, but experimental features are not subject to the support SLA of official GA features.
Required privileges which should be added to a custom role:
You can get information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression.
- (Optional, string) The unique identifier of the trained inference model.
(Optional, boolean) Specifies what to do when the request:
- Contains wildcard expressions and there are no data frame analytics jobs that match.
_allstring or no identifiers and there are no matches.
- Contains wildcard expressions and there are only partial matches.
The default value is
true, which returns an empty
data_frame_analyticsarray when there are no matches and the subset of results when there are partial matches. If this parameter is
false, the request returns a
404status code when there are no matches or only partial matches.
Specifies whether the included model definition should be returned as a JSON map
true) or in a custom compressed format (
false). Defaults to
Skips the specified number of data frame analytics jobs. The default value is
Specifies if the model definition should be returned in the response. Defaults
true, only a single model must match the ID patterns provided, otherwise a bad request is returned.
Specifies the maximum number of data frame analytics jobs to obtain. The default value
- (Optional, string) A comma delimited string of tags. A inference model can have many tags, or none. When supplied, only inference models that contain all the supplied tags are returned.
true, this code indicates that more than one models match the ID pattern.
false, this code indicates that there are no resources that match the request or only partial matches for the request.
The following example gets configuration information for all the trained models: