detect_multivariate_batch_anomalies
Creates, updates, deletes, gets or lists a detect_multivariate_batch_anomalies resource.
Overview
| Name | detect_multivariate_batch_anomalies |
| Type | Resource |
| Id | azure.ai_anomaly_detector.detect_multivariate_batch_anomalies |
Fields
The following fields are returned by SELECT queries:
SELECT not supported for this resource, use SHOW METHODS to view available operations for the resource.
Methods
The following methods are available for this resource:
| Name | Accessible by | Required Params | Optional Params | Description |
|---|---|---|---|---|
detect_multivariate_batch_anomaly | exec | model_id, endpoint, api_version, dataSource, topContributorCount, startTime, endTime | Detect Multivariate Anomaly. Submit multivariate anomaly detection task with the modelId of trained model and inference data, the input schema should be the same with the training request. The request will complete asynchronously and return a resultId to query the detection result.The request should be a source link to indicate an externally accessible Azure storage Uri, either pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob storage. |
Parameters
Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.
| Name | Datatype | Description |
|---|---|---|
api_version | string | The service endpoint host (no scheme), e.g. myaccount.table.cosmos.azure.com:443 - value of the client ApiVersion parameter. (default: ) |
endpoint | string | The service endpoint host (no scheme), e.g. myaccount.table.cosmos.azure.com:443 - value of the client Endpoint parameter. (default: ) |
model_id | string | Model identifier. Required. |
Lifecycle Methods
- detect_multivariate_batch_anomaly
Detect Multivariate Anomaly. Submit multivariate anomaly detection task with the modelId of trained model and inference data, the input schema should be the same with the training request. The request will complete asynchronously and return a resultId to query the detection result.The request should be a source link to indicate an externally accessible Azure storage Uri, either pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob storage.
EXEC azure.ai_anomaly_detector.detect_multivariate_batch_anomalies.detect_multivariate_batch_anomaly
@model_id='{{ model_id }}' --required,
@endpoint='{{ endpoint }}' --required,
@api_version='{{ api_version }}' --required
@@json=
'{
"dataSource": "{{ dataSource }}",
"topContributorCount": {{ topContributorCount }},
"startTime": "{{ startTime }}",
"endTime": "{{ endTime }}"
}'
;