Refresh article

Author: cdpark

articles/machine-learning/v1/how-to-run-batch-predictions-designer.md

@@ -1,5 +1,5 @@
 ---
-title: Run batch predictions using Azure Machine Learning designer
+title: Run Batch Predictions Using Designer
 titleSuffix: Azure Machine Learning
 description: Learn how to create a batch prediction pipeline. Deploy the pipeline as a parameterized web service, and trigger it from any HTTP library.
 services: machine-learning
@@ -8,7 +8,7 @@ ms.subservice: mlops
 ms.author: lagayhar
 author: lgayhardt
 ms.reviewer: keli19
-ms.date: 05/10/2022
+ms.date: 06/16/2025
 ms.topic: how-to
 ms.custom: UpdateFrequency5, designer
 ---
@@ -19,101 +19,98 @@ ms.custom: UpdateFrequency5, designer
 
 In this article, you learn how to use the designer to create a batch prediction pipeline. Batch prediction lets you continuously score large datasets on-demand using a web service that can be triggered from any HTTP library.
 
-In this how-to, you learn to do the following tasks:
+In this how-to guide, you learn to do the following tasks:
 
 > [!div class="checklist"]
-> * Create and publish a batch inference pipeline
-> * Consume a pipeline endpoint
-> * Manage endpoint versions
+> * Create and publish a batch inference pipeline.
+> * Consume a pipeline endpoint.
+> * Manage endpoint versions.
 
-To learn how to set up batch scoring services using the SDK, see the accompanying [tutorial on pipeline batch scoring](../tutorial-pipeline-batch-scoring-classification.md).
+To learn how to set up batch scoring services using the SDK, see [Tutorial: Build an Azure Machine Learning pipeline for image classification](../tutorial-pipeline-python-sdk.md).
 
 ## Prerequisites
 
 This how-to assumes you already have a training pipeline. For a guided introduction to the designer, complete [part one of the designer tutorial](tutorial-designer-automobile-price-train-score.md).
 
 > [!IMPORTANT]
-> If you do not see graphical elements mentioned in this document, such as buttons in studio or designer, you may not have the right level of permissions to the workspace. Please contact your Azure subscription administrator to verify that you have been granted the correct level of access. For more information, see [Manage users and roles](../how-to-assign-roles.md).
+> If you don't see graphical elements mentioned in this document, such as buttons in studio or designer, you might not have the right level of permissions to the workspace. Contact your Azure subscription administrator to verify that you have been granted the correct level of access. For more information, see [Manage users and roles](../how-to-assign-roles.md).
 
 ## Create a batch inference pipeline
 
 Your training pipeline must be run at least once to be able to create an inferencing pipeline.
 
-1. Go to the **Designer** tab in your workspace.
+1. Sign in to the Machine Learning studio, then select **Designer**.
 
 1. Select the training pipeline that trains the model you want to use to make prediction.
 
-1. **Submit** the pipeline.
-
-![Submit the pipeline](./media/how-to-run-batch-predictions-designer/run-training-pipeline.png)
+1. Select **Configure & Submit**.
 
  :::image type="content" source="./media/how-to-run-batch-predictions-designer/run-training-pipeline.png" alt-text="Screenshot showing the set up pipeline job with the experiment drop-down and submit button highlighted." lightbox= "./media/how-to-run-batch-predictions-designer/run-training-pipeline.png":::
 
-You'll see a submission list on the left of canvas. You can select the job detail link to go to the job detail page, and after the training pipeline job completes, you can create a batch inference pipeline.
-
- :::image type="content" source="./media/how-to-run-batch-predictions-designer/submission-list.png" alt-text="Screenshot showing the submitted job list." lightbox= "./media/how-to-run-batch-predictions-designer/submission-list.png":::
+You can select the job detail link to go to the job detail page, and after the training pipeline job completes, you can create a batch inference pipeline.
 
-1. In job detail page, above the canvas, select the dropdown **Create inference pipeline**. Select **Batch inference pipeline**.
+1. Select **Jobs** in the sidebar menu and choose your job. Above the canvas, select the dropdown **Create inference pipeline**. Select **Batch inference pipeline**.
 
     > [!NOTE]
     > Currently auto-generating inference pipeline only works for training pipeline built purely by the designer built-in components.
 
     :::image type="content" source="./media/how-to-run-batch-predictions-designer/create-batch-inference.png" alt-text="Screenshot of the create inference pipeline drop-down with batch inference pipeline highlighted." lightbox= "./media/how-to-run-batch-predictions-designer/create-batch-inference.png":::
 
-    It will create a batch inference pipeline draft for you. The batch inference pipeline draft uses the trained model as **MD-** node and transformation as **TD-** node from the training pipeline job.
+    It creates a batch inference pipeline draft for you. The batch inference pipeline draft uses the trained model as **MD-** node and transformation as **TD-** node from the training pipeline job.
 
     You can also modify this inference pipeline draft to better handle your input data for batch inference.
 
      :::image type="content" source="./media/how-to-run-batch-predictions-designer/batch-inference-draft.png" alt-text="Screenshot showing a batch inference pipeline draft." lightbox= "./media/how-to-run-batch-predictions-designer/batch-inference-draft.png":::
 
-### Add a pipeline parameter
+### Add a pipeline input
 
-To create predictions on new data, you can either manually connect a different dataset in this pipeline draft view or create a parameter for your dataset. Parameters let you change the behavior of the batch inferencing process at runtime.
+To create predictions on new data, you can either manually connect a different dataset in this pipeline draft view or create an input parameter for your dataset. Inputs let you change the behavior of the batch inferencing process at runtime.
 
-In this section, you create a dataset parameter to specify a different dataset to make predictions on.
+In this section, you create a pipeline input to specify a different dataset to make predictions on.
 
 1. Select the dataset component.
 
-1. A pane will appear to the right of the canvas. At the bottom of the pane, select **Set as pipeline parameter**.
-   
-    Enter a name for the parameter, or accept the default value.
+1. A pane appears to the right of the canvas. At the bottom of the pane, select **Set as pipeline input**.
 
-     :::image type="content" source="./media/how-to-run-batch-predictions-designer/create-pipeline-parameter.png" alt-text="Screenshot of cleaned dataset tab with set as pipeline parameter checked." lightbox= "./media/how-to-run-batch-predictions-designer/create-pipeline-parameter.png":::
+    Enter a name for the parameter, or accept the default value.
 
+     :::image type="content" source="./media/how-to-run-batch-predictions-designer/create-pipeline-parameter.png" alt-text="Screenshot of cleaned dataset tab with set as pipeline input checked." lightbox= "./media/how-to-run-batch-predictions-designer/create-pipeline-parameter.png":::
 
-1. Submit the batch inference pipeline and go to job detail page by selecting the job link in the left pane.
+1. Submit the batch inference pipeline.
 
 ## Publish your batch inference pipeline
 
-Now you're ready to deploy the inference pipeline. This will deploy the pipeline and make it available for others to use.
+Now you're ready to deploy the inference pipeline. This deploys the pipeline and makes it available for others to use.
+
+1. Select **Jobs** in the sidebar menu and select your running job.
 
-1. Select the **Publish** button.
+1. Select the **Publish** button in the ribbon menu.
 
-1. In the dialog that appears, expand the drop-down for **PipelineEndpoint**, and select **New PipelineEndpoint**.
+1. In the dialog that appears, **Create new**.
 
 1. Provide an endpoint name and optional description.
 
-    Near the bottom of the dialog, you can see the parameter you configured with a default value of the dataset ID used during training.
+    Near the bottom of the dialog, you can see the input you configured with a default value of the dataset ID used during training.
 
 1. Select **Publish**.
 
 :::image type="content" source="./media/how-to-run-batch-predictions-designer/publish-inference-pipeline.png" alt-text="Screenshot of set up published pipeline." lightbox= "./media/how-to-run-batch-predictions-designer/publish-inference-pipeline.png":::
 
 ## Consume an endpoint
 
-Now, you have a published pipeline with a dataset parameter. The pipeline will use the trained model created in the training pipeline to score the dataset you provide as a parameter.
+Now, you have a published pipeline with a dataset parameter. The pipeline uses the trained model created in the training pipeline to score the dataset you provide as a parameter.
 
 ### Submit a pipeline job
 
-In this section, you'll set up a manual pipeline job and alter the pipeline parameter to score new data.
+In this section, you set up a manual pipeline job and alter the pipeline parameter to score new data.
 
-1. After the deployment is complete, go to the **Endpoints** section.
+1. After the deployment is complete, select **Pipelines** in the sidebar menu.
 
-1. Select **Pipeline endpoints**.
+1. Select the **Pipeline endpoints** tab.
 
 1. Select the name of the endpoint you created.
 
-:::image type="content" source="./media/how-to-run-batch-predictions-designer/manage-endpoints.png" alt-text="Screenshot of the pipeline endpoint tab." :::
+    :::image type="content" source="./media/how-to-run-batch-predictions-designer/manage-endpoints.png" alt-text="Screenshot of the pipeline endpoint tab." :::
 
 1. Select **Published pipelines**.
 
@@ -122,13 +119,13 @@ In this section, you'll set up a manual pipeline job and alter the pipeline para
 1. Select the pipeline you published.
 
     The pipeline details page shows you a detailed job history and connection string information for your pipeline.
-    
+
 1. Select **Submit** to create a manual run of the pipeline.
 
     :::image type="content" source="./media/how-to-run-batch-predictions-designer/submit-manual-run.png" alt-text="Screenshot of set up pipeline job with parameters highlighted." lightbox= "./media/how-to-run-batch-predictions-designer/submit-manual-run.png" :::
-    
+
 1. Change the parameter to use a different dataset.
-    
+
 1. Select **Submit** to run the pipeline.
 
 ### Use the REST endpoint
@@ -139,45 +136,43 @@ You can find the REST endpoint of a pipeline endpoint in the job overview panel.
 
 You can also consume a published pipeline in the **Published pipelines** page. Select a published pipeline and you can find the REST endpoint of it in the **Published pipeline overview** panel to the right of the graph. 
 
-To make a REST call, you'll need an OAuth 2.0 bearer-type authentication header. See the following [tutorial section](../tutorial-pipeline-batch-scoring-classification.md#publish-and-run-from-a-rest-endpoint) for more detail on setting up authentication to your workspace and making a parameterized REST call.
+To make a REST call, you need an OAuth 2.0 bearer-type authentication header. See the following [tutorial section](../tutorial-pipeline-batch-scoring-classification.md#publish-and-run-from-a-rest-endpoint) for more detail on setting up authentication to your workspace and making a parameterized REST call.
 
 ## Versioning endpoints
 
-The designer assigns a version to each subsequent pipeline that you publish to an endpoint. You can specify the pipeline version that you want to execute as a parameter in your REST call. If you don't specify a version number, the designer will use the default pipeline.
+The designer assigns a version to each subsequent pipeline that you publish to an endpoint. You can specify the pipeline version that you want to execute as a parameter in your REST call. If you don't specify a version number, the designer uses the default pipeline.
 
 When you publish a pipeline, you can choose to make it the new default pipeline for that endpoint.
 
 :::image type="content" source="./media/how-to-run-batch-predictions-designer/set-default-pipeline.png" alt-text="Screenshot of set up published pipeline with set as default pipeline for this endpoint checked." :::
 
 You can also set a new default pipeline in the **Published pipelines** tab of your endpoint.
 
-:::image type="content" source="./media/how-to-run-batch-predictions-designer/set-new-default-pipeline.png" alt-text="Screenshot of sample pipeline tab with set as default highlighted." :::
-
 ## Update pipeline endpoint
 
 If you make some modifications in your training pipeline, you might want to update the newly trained model to the pipeline endpoint.
 
 1. After your modified training pipeline completes successfully, go to the job detail page.
 
-1. Right click **Train Model** component and select **Register data**
+1. Right-click the **Train Model** component and select **Register data**.
 
     :::image type="content" source="./media/how-to-run-batch-predictions-designer/register-train-model-as-dataset.png" alt-text="Screenshot of the train model component options with register data highlighted." lightbox= "./media/how-to-run-batch-predictions-designer/register-train-model-as-dataset.png" :::
 
-    Input name and select **File** type.
+    Input a name and select a **File** type.
 
     :::image type="content" source="./media/how-to-run-batch-predictions-designer/register-train-model-as-dataset-2.png" alt-text="Screenshot of register as data asset with new data asset selected." lightbox= "./media/how-to-run-batch-predictions-designer/register-train-model-as-dataset-2.png" :::
 
-1. Find the previous batch inference pipeline draft, or you can just **Clone** the published pipeline into a new draft.
+1. Find the previous batch inference pipeline draft, or **Clone** the published pipeline into a new draft.
 
-1. Replace the **MD-** node in the inference pipeline draft with the registered data in the step above.
+1. Replace the **MD-** node in the inference pipeline draft with the registered data in the preceding step.
 
-    :::image type="content" source="./media/how-to-run-batch-predictions-designer/update-inference-pipeline-draft.png" alt-text="Screenshot of updating the inference pipeline draft with the registered data in the step above." :::
+    :::image type="content" source="./media/how-to-run-batch-predictions-designer/update-inference-pipeline-draft.png" alt-text="Screenshot of updating the inference pipeline draft with the registered data." :::
 
 1. Updating data transformation node **TD-** is the same as the trained model.
 
-1. Then you can submit the inference pipeline with the updated model and transformation, and publish again.
+1. Submit the inference pipeline with the updated model and transformation, and publish again.
 
-## Next steps
+## Related content
 
-* Follow the [designer tutorial to train and deploy a regression model](tutorial-designer-automobile-price-train-score.md).
-* For how to publish and run a published pipeline using the SDK v1, see the [How to deploy pipelines](how-to-deploy-pipelines.md?view=azureml-api-1&preserve-view=true) article.
+* [Tutorial: Train a no-code regression model using designer](tutorial-designer-automobile-price-train-score.md)
+* [Publish machine learning pipelines](how-to-deploy-pipelines.md?view=azureml-api-1&preserve-view=true)