How to use batch endpoints in Azure Machine Learning studio

In this article, you learn how to use batch endpoints to do batch scoring in Azure Machine Learning studio. For more, see What are Azure Machine Learning endpoints?.

In this article, you learn about:

• An Azure subscription - If you don't have an Azure subscription, create an account before you begin. Try Azure Machine Learning today.

• The example repository - Clone the AzureML Example repository. This article uses the assets in /cli/endpoints/batch.

• A compute target where you can run batch scoring workflows. For more information on creating a compute target, see Create compute targets in Azure Machine Learning studio.

• Register machine learning model.

There are two ways to create Batch Endpoints in Azure Machine Learning studio:

• From the Endpoints page, select Batch Endpoints and then select + Create.

  

OR

• From the Models page, select the model you want to deploy and then select Deploy to batch endpoint.

  

Complete all the steps in the wizard to create a batch endpoint and deployment.

After a batch endpoint is created, select it from the Endpoints page to view the details.

A batch scoring workload runs as an offline job. By default, batch scoring stores the scoring outputs in blob storage. You can also configure the outputs location and overwrite some of the settings to get the best performance.

1. Select + Create job:

   

2. You can update the default deployment while submitting a job from the drop-down:

   

Some settings can be overwritten when you start a batch scoring job. For example, you might overwrite settings to make better use of the compute resource, or to improve performance. To override settings, select Override deployment settings and provide the settings. For more information, see Use batch endpoints.

You have two options to specify the data inputs in Azure machine learning studio:

• Use a registered dataset:

  Note

  During Preview, only FileDataset is supported.

  

OR

• Use a datastore:

  You can specify AzureML registered datastore or if your data is publicly available, specify the public path.

  

By default, the batch scoring results are stored in the default blob store for the workspace. Results are in a folder named after the job name (a system-generated GUID).

To change where the results are stored, providing a blob store and output path when you start a job.

To see a summary of all the submitted jobs for an endpoint, select the endpoint and then select the Runs tab.

To learn how to view the scoring results, see Use batch endpoints.

In Azure machine learning studio, there are two ways to add a deployment to an existing batch endpoint:

• From the Endpoints page, select the batch endpoint to add a new deployment to. Select + Add deployment, and complete the wizard to add a new deployment.

  

OR

• From the Models page, select the model you want to deploy. Then select Deploy to batch endpoint option from the drop-down. In the wizard, on the Endpoint screen, select Existing. Complete the wizard to add the new deployment.

  

If an endpoint has multiple deployments, one of the deployments is the default. The default deployment receives 100% of the traffic to the endpoint. To change the default deployment, use the following steps:

1. Select the endpoint from the Endpoints page.
2. Select Update default deployment. From the Details tab, select the deployment you want to set as default and then select Update.

To delete an endpoint, select the endpoint from the Endpoints page and then select delete.

To delete a deployment, select the endpoint from the Endpoints page, select the deployment, and then select delete.

In this article, you learned how to create and call batch endpoints. See these other articles to learn more about Azure Machine Learning:

• Troubleshooting batch endpoints
• Deploy and score a machine learning model with a managed online endpoint