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
}
}