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and inference data. The input schema should be the same with the training
request. The request will finish asynchronously and return a resultId value to
query the detection result. The request should be a source link to indicate an
externally accessible Azure Storage URI that either points to an Azure Blob
Storage folder or points to a CSV file in Azure Blob Storage." }, "response": [ { "id": "e72b20f7-bf28-4014-a66b-720a88ef33ef", "name": "The request has been accepted for processing, but processing has not yet completed.", "originalRequest": { "method": "POST", "header": [ { "key": "Content-Type", "value": "application/json" }, { "key": "Accept", "value": "application/json" }, { "description": "Added as a part of security scheme: apikey", "key": "Ocp-Apim-Subscription-Key", "value": "" } ], "body": { "mode": "raw", "raw": "{\n \"dataSource\": \"\",\n \"startTime\": \"\",\n \"endTime\": \"\",\n \"topContributorCount\": 10\n}", "options": { "raw": { "headerFamily": "json", "language": "json" } } }, "url": { "raw": "{{baseUrl}}/multivariate/models/{{modelId}}:detect-batch", "host": [ "{{baseUrl}}" ], "path": [ "multivariate", "models", "{{modelId}}:detect-batch" ] } }, "status": "Accepted", "code": 202, "_postman_previewlanguage": "json", "header": [ { "key": "Content-Type", "value": 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The request must
include a source parameter to indicate an Azure Blob
Storage URI that's accessible to the service. There are two types of data input. The Blob Storage URI can point to an Azure Blob
Storage folder that contains multiple CSV files, where each CSV file has
two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file that has all the variables and a
time stamp column.
The model object will be created and returned in the response, but the
training process happens asynchronously. To check the training status, call
GetMultivariateModel with the modelId value and check the status field in the
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