Consume pipeline endpoints from client applications

Important

Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.

Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources.

ML Studio (classic) documentation is being retired and may not be updated in the future.

In this article, you learn how to integrate client applications with Azure Machine Learning endpoints. For more information on writing application code, see Consume an Azure Machine Learning endpoint.

This article is part of the Studio (classic) to Azure Machine Learning migration series. For more information on migrating to Azure Machine Learning, see the migration overview article.

Prerequisites

Consume a real-time endpoint

If you deployed your model as a real-time endpoint, you can find its REST endpoint, and pre-generated consumption code in C#, Python, and R:

  1. Go to Azure Machine Learning studio (ml.azure.com).
  2. Go the Endpoints tab.
  3. Select your real-time endpoint.
  4. Select Consume.

Note

You can also find the Swagger specification for your endpoint in the Details tab. Use the Swagger definition to understand your endpoint schema. For more information on Swagger definition, see Swagger official documentation.

Consume a pipeline endpoint

There are two ways to consume a pipeline endpoint:

  • REST API calls
  • Integration with Azure Data Factory

Use REST API calls

Call the REST endpoint from your client application. You can use the Swagger specification for your endpoint to understand its schema:

  1. Go to Azure Machine Learning studio (ml.azure.com).
  2. Go the Endpoints tab.
  3. Select Pipeline endpoints.
  4. Select your pipeline endpoint.
  5. In the Pipeline endpoint overview pane, select the link under REST endpoint documentation.

Use Azure Data Factory

You can call your Azure Machine Learning pipeline as a step in an Azure Data Factory pipeline. For more information, see Execute Azure Machine Learning pipelines in Azure Data Factory.

Next steps

In this article, you learned how to find schema and sample code for your pipeline endpoints. For more information on consuming endpoints from the client application, see Consume an Azure Machine Learning endpoint.

See the rest of the articles in the Azure Machine Learning migration series:

  1. Migration overview.
  2. Migrate dataset.
  3. Rebuild a Studio (classic) training pipeline.
  4. Rebuild a Studio (classic) web service.
  5. Integrate an Azure Machine Learning web service with client apps.
  6. Migrate Execute R Script.