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 (workspace and web service plan). Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) experiments and web services.
- See information on moving machine learning projects from ML Studio (classic) to Azure Machine Learning.
- Learn more about Azure Machine Learning
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.
This article is part of the ML Studio (classic) to Azure Machine Learning migration series. For more information on migrating to Azure Machine Learning, see the migration overview article.
Prerequisites
- An Azure account with an active subscription. Create an account for free.
- An Azure Machine Learning workspace. Create workspace resources.
- An Azure Machine Learning real-time endpoint or pipeline endpoint.
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:
- Go to Azure Machine Learning studio (ml.azure.com).
- Go the Endpoints tab.
- Select your real-time endpoint.
- 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:
- Go to Azure Machine Learning studio (ml.azure.com).
- Go the Endpoints tab.
- Select Pipeline endpoints.
- Select your pipeline endpoint.
- 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 authenticating to an endpoint, see Authenticate to an online endpoint.
See the rest of the articles in the Azure Machine Learning migration series:
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