Update model snapshots API
editUpdate model snapshots API
editUpdates certain properties of a snapshot.
Request
editPOST _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>/_update
Prerequisites
editRequires the manage_ml
cluster privilege. This privilege is included in the
machine_learning_admin
built-in role.
Path parameters
edit-
<job_id>
- (Required, string) Identifier for the anomaly detection job.
-
<snapshot_id>
- (Required, string) Identifier for the model snapshot.
Request body
editThe following properties can be updated after the model snapshot is created:
-
description
- (Optional, string) A description of the model snapshot.
-
retain
-
(Optional, Boolean)
If
true
, this snapshot will not be deleted during automatic cleanup of snapshots older thanmodel_snapshot_retention_days
. However, this snapshot will be deleted when the job is deleted. The default value isfalse
.
Examples
editresp = client.ml.update_model_snapshot( job_id="it_ops_new_logs", snapshot_id="1491852978", description="Snapshot 1", retain=True, ) print(resp)
response = client.ml.update_model_snapshot( job_id: 'it_ops_new_logs', snapshot_id: 1_491_852_978, body: { description: 'Snapshot 1', retain: true } ) puts response
const response = await client.ml.updateModelSnapshot({ job_id: "it_ops_new_logs", snapshot_id: 1491852978, description: "Snapshot 1", retain: true, }); console.log(response);
POST _ml/anomaly_detectors/it_ops_new_logs/model_snapshots/1491852978/_update { "description": "Snapshot 1", "retain": true }
When the snapshot is updated, you receive the following results:
{ "acknowledged": true, "model": { "job_id": "it_ops_new_logs", "timestamp": 1491852978000, "description": "Snapshot 1", ... "retain": true } }