Merge pull request #5453 from cdpark/refresh-ml-designer-2

Feature 438779: Q&M: Azure ML Designer doc Freshness - batch 2

Author: JillGrant615

articles/machine-learning/v1/media/tutorial-designer-automobile-price-deploy/create-real-time-inference.png

@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Use designer to deploy no-code models'
+title: Use Designer to Deploy No-Code Models
 titleSuffix: Azure Machine Learning
 description: Learn how to deploy a machine learning model to predict car prices with the Azure Machine Learning designer.
 ms.reviewer: None
@@ -9,28 +9,28 @@ services: machine-learning
 ms.service: azure-machine-learning
 ms.subservice: core
 ms.topic: tutorial
-ms.date: 03/04/2024
+ms.date: 06/09/2025
 ms.custom: UpdateFrequency5, designer
 ---
 
-# Tutorial: Use the designer to deploy a machine learning model
+# Tutorial: Deploy a machine learning model using designer
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 In [part one of this tutorial](tutorial-designer-automobile-price-train-score.md), you trained a linear regression model that predicts car prices. In this second part, you use the Azure Machine Learning designer to deploy the model so that others can use it.
 
->[!Note]
-> The designer supports two types of components: classic prebuilt components (v1) and custom components (v2). These two types of components are NOT compatible.
+> [!NOTE]
+> Designer supports two types of components: classic prebuilt components (v1) and custom components (v2). These two types of components are NOT compatible. 
 >
->*Classic prebuilt components* provide prebuilt components mainly for data processing and traditional machine learning tasks like regression and classification. This type of component continues to be supported but no new components will be added.
+>Classic prebuilt components are intended primarily for data processing and traditional machine learning tasks like regression and classification. This type of component continues to be supported but will not have any new components added. 
 >
->*Custom components* allow you to wrap your own code as a component. They support sharing components across workspaces and seamless authoring across Machine Learning Studio, CLI v2, and SDK v2 interfaces.
+>Custom components allow you to wrap your own code as a component. It supports sharing components across workspaces and seamless authoring across Studio, CLI v2, and SDK v2 interfaces. 
 >
->For new projects, we advise that you use custom components, which are compatible with Azure Machine Learning v2 and will keep receiving new updates.
+>For new projects, we highly suggest that you use custom components, which are compatible with Azure Machine Learning V2 and will keep receiving new updates. 
 >
 >This article applies to classic prebuilt components and isn't compatible with CLI v2 and SDK v2.
 
-In this tutorial, you:
+In this tutorial, you learn how to:
 
 > [!div class="checklist"]
 > * Create a real-time inference pipeline.
@@ -43,7 +43,7 @@ In this tutorial, you:
 Complete [part one of the tutorial](tutorial-designer-automobile-price-train-score.md) to learn how to train and score a machine learning model in the designer.
 
 > [!IMPORTANT]
-> 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. 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 real-time inference pipeline
 
@@ -54,7 +54,7 @@ To deploy your pipeline, you must first convert the training pipeline into a rea
 
 ### Create a real-time inference pipeline
 
-1. Select **Pipelines** from the side navigation panel, then open the pipeline job that you created. On the detail page, above the pipeline canvas, select the ellipses **...** then choose **Create inference pipeline** > **Real-time inference pipeline**.
+1. Select **Jobs** from the sidebar menu, then open the pipeline job that you created. On the detail page, above the pipeline canvas, select the ellipses **...** then choose **Create inference pipeline** > **Real-time inference pipeline**.
 
      :::image type="content" source="media/tutorial-designer-automobile-price-deploy/create-real-time-inference.png" alt-text="Screenshot of create inference pipeline in pipeline job detail page." lightbox="media/tutorial-designer-automobile-price-deploy/create-real-time-inference.png":::
 
@@ -81,24 +81,24 @@ To deploy your pipeline, you must first convert the training pipeline into a rea
 
 1. Select **Deploy** in the job detail page.
 
-     :::image type="content" source="./media/tutorial-designer-automobile-price-deploy/deploy-in-job-detail-page.png" alt-text="Screenshot showing deploying in job detail page.":::
+     :::image type="content" source="./media/tutorial-designer-automobile-price-deploy/deploy-in-job-detail-page.png" alt-text="Screenshot showing deploying in job detail page." lightbox="./media/tutorial-designer-automobile-price-deploy/deploy-in-job-detail-page.png":::
 
 ## Create an inferencing cluster
 
 In the dialog box that appears, you can select from any existing Azure Kubernetes Service (AKS) clusters to deploy your model to. If you don't have an AKS cluster, use the following steps to create one.
 
-1. Go to the **Compute** page by selecting **Compute** in the dialog box.
+1. Go to the **Compute** page by selecting **Compute** in the sidebar menu.
 
-1. On the navigation ribbon, select **Kubernetes Clusters** > **+ New**.
+1. On the navigation ribbon, select **Kubernetes Clusters**. Then select **+ New** > **AksCompute**.
 
     :::image type="content" source="./media/tutorial-designer-automobile-price-deploy/new-inference-cluster.png" alt-text="Screenshot showing how to get to the new inference cluster pane.":::
 
 1. In the inference cluster pane, configure a new Kubernetes Service.
 
-1. Enter *aks-compute* for the **Compute name**.
-
 1. Select a nearby region that's available for the **Region**.
 
+1. On the next screen, enter *aks-compute* for the **Compute name**.
+
 1. Select **Create**.
 
     > [!NOTE]
@@ -116,19 +116,19 @@ After your AKS service finishes provisioning, return to the real-time inferencin
 
      :::image type="content" source="./media/tutorial-designer-automobile-price-deploy/setup-endpoint.png" alt-text="Screenshot showing how to set up a new real-time endpoint.":::
 
-    You can also change the **Advanced** setting for your real-time endpoint.
+    You can also change the **Advanced** settings for your real-time endpoint.
 
     |Advanced setting|Description|
     |---|---|
-    |Enable Application Insights diagnostics and data collection| Allows Azure Application Insights to collect data from the deployed endpoints. <br> By default: false. |
-    |Scoring timeout| A timeout in milliseconds to enforce for scoring calls to the web service. <br> By default: 60000.|
-    |Auto scale enabled| Allows autoscaling for the web service.<br> By default: true.|
-    |Min replicas| The minimum number of containers to use when autoscaling this web service.<br> By default: 1.|
-    |Max replicas| The maximum number of containers to use when autoscaling this web service.<br> By default: 10.|
-    |Target utilization|The target utilization (as a percentage) that the autoscaler should attempt to maintain for this web service.<br> By default: 70.|
-    |Refresh period|How often (in seconds) the autoscaler attempts to scale this web service.<br> By default: 1.|
-    |CPU reserve capacity|The number of CPU cores to allocate for this web service.<br> By default: 0.1.|
-    |Memory reserve capacity|The amount of memory (in GB) to allocate for this web service.<br> By default: 0.5.|
+    |Enable Application Insights diagnostics and data collection| Allows Azure Application Insights to collect data from the deployed endpoints. <br> By default: false |
+    |Scoring timeout| A timeout in milliseconds to enforce for scoring calls to the web service. <br> By default: 60000|
+    |Auto scale enabled| Allows autoscaling for the web service.<br> By default: true|
+    |Min replicas| The minimum number of containers to use when autoscaling this web service.<br> By default: 1|
+    |Max replicas| The maximum number of containers to use when autoscaling this web service.<br> By default: 10|
+    |Target utilization|The target utilization (as a percentage) that the autoscaler should attempt to maintain for this web service.<br> By default: 70|
+    |Refresh period|How often (in seconds) the autoscaler attempts to scale this web service.<br> By default: 1|
+    |CPU reserve capacity|The number of CPU cores to allocate for this web service.<br> By default: 0.1|
+    |Memory reserve capacity|The amount of memory (in GB) to allocate for this web service.<br> By default: 0.5|
 
 1. Select **Deploy**.
 
@@ -144,13 +144,13 @@ After your AKS service finishes provisioning, return to the real-time inferencin
 
 After deployment finishes, you can view your real-time endpoint by going to the **Endpoints** page.
 
-1. On the **Endpoints** page, select the endpoint you deployed.
+1. Select **Endpoints** on the sidebar menu, then select the endpoint you deployed.
 
-    In the **Details** tab, you can see more information such as the REST URI, Swagger definition, status, and tags.
+    - In the **Details** tab, you can see more information such as the REST URI, Swagger definition, status, and tags.
 
-    In the **Consume** tab, you can find sample consumption code, security keys, and set authentication methods.
+    - In the **Consume** tab, you can find sample consumption code, security keys, and set authentication methods.
 
-    In the **Deployment logs** tab, you can find the detailed deployment logs of your real-time endpoint.
+    - In the **Deployment logs** tab, you can find the detailed deployment logs of your real-time endpoint.
 
 1. To test your endpoint, go to the **Test** tab. From here, you can enter test data and select **Test** verify the output of your endpoint.
 
@@ -166,35 +166,35 @@ You can update the online endpoint with new model trained in the designer. On th
 
 1. After you submit the modified training pipeline, go to the job detail page.
 
-1. When the job completes, right click **Train Model** and select **Register data**.
+1. When the job completes, right-click **Train Model** and select **Register data**.
 
      :::image type="content" source="media/how-to-run-batch-predictions-designer/register-train-model-as-dataset.png" alt-text="Screenshot showing register trained model as dataset." 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 **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 a data asset with new data asset selected.":::
+    :::image type="content" source="./media/how-to-run-batch-predictions-designer/register-train-model-as-dataset-2.png" alt-text="Screenshot of register as a data asset with new data asset selected." lightbox="./media/how-to-run-batch-predictions-designer/register-train-model-as-dataset-2.png":::
 
-1. After the dataset registers successfully, open your inference pipeline draft, or clone the previous inference pipeline job into a new draft. In the inference pipeline draft, replace the previous trained model shown as **MD-XXXX** node connected to the **Score Model** component with the newly registered dataset.
+1. After the dataset registers successfully, open your inference pipeline draft, or clone the previous inference pipeline job into a new draft. In the inference pipeline draft, replace the previous trained model shown as **MD-xxxx** node connected to the **Score Model** component with the newly registered dataset.
 
     :::image type="content" source="media/tutorial-designer-automobile-price-deploy/modify-inference-pipeline.png" alt-text="Screenshot showing how to modify inference pipeline." lightbox="media/tutorial-designer-automobile-price-deploy/modify-inference-pipeline.png":::
 
-1. If you need to update the data preprocessing part in your training pipeline, and would like to update that into the inference pipeline, the processing is similar as steps above.
+1. If you need to update the data preprocessing part in your training pipeline, and would like to update that into the inference pipeline, the processing is similar to the preceding steps.
 
     You just need to register the transformation output of the transformation component as dataset.
 
-    Then, manually replace the **TD-** component in the inference pipeline with the registered dataset.
+    Then, manually replace the **TD-xxxx** component in the inference pipeline with the registered dataset.
 
     :::image type="content" source="media/tutorial-designer-automobile-price-deploy/replace-td-module.png" alt-text="Screenshot showing how to replace transformation component." lightbox="media/tutorial-designer-automobile-price-deploy/replace-td-module.png":::
 
 1. After modifying your inference pipeline with the newly trained model or transformation, submit it. When the job is completed, deploy it to the existing online endpoint deployed previously.
 
-    :::image type="content" source="./media/tutorial-designer-automobile-price-deploy/deploy-to-existing-endpoint.png" alt-text="Screenshot showing how to replace existing real-time endpoint.":::
+    :::image type="content" source="./media/tutorial-designer-automobile-price-deploy/deploy-to-existing-endpoint.png" alt-text="Screenshot showing how to replace existing real-time endpoint." lightbox="./media/tutorial-designer-automobile-price-deploy/deploy-to-existing-endpoint.png":::
 
 ## Limitations
 
-* Due to datastore access limitation, if your inference pipeline contains **Import Data** or **Export Data** components, they're auto-removed when deployed to real-time endpoint.
+* Due to datastore access limitation, if your inference pipeline contains **Import Data** or **Export Data** components, they're autoremoved when deployed to a real-time endpoint.
 
-* If you have datasets in the real-time inference pipeline and want to deploy it to real-time endpoint, currently this flow only supports datasets registered from **Blob** datastore. If you want to use datasets from other type datastores, you can use **Select Column** to connect with your initial dataset with settings of selecting all columns, register the outputs of **Select Column** as File dataset and then replace the initial dataset in the real-time inference pipeline with this newly registered dataset.
+* If you have datasets in the real-time inference pipeline and want to deploy to a real-time endpoint, currently this flow only supports datasets registered from **Blob** datastore. If you want to use datasets from other type datastores, you can use **Select Column** to connect with your initial dataset with settings of selecting all columns, register the outputs of **Select Column** as File dataset and then replace the initial dataset in the real-time inference pipeline with this newly registered dataset.
 
 * If your inference graph contains **Enter Data Manually** component that isn't connected to the same port as **Web Service Input** component, the **Enter Data Manually** component isn't executed during HTTP call processing. A workaround is to register the outputs of that **Enter Data Manually** component as a dataset, then in the inference pipeline draft, replace the **Enter Data Manually** component with the registered dataset.