Merge pull request #95391 from paulth1/automobile-price-train-score-and-deploy

edit pass: Automobile price train score and deploy

Author: ShawnJackson

articles/machine-learning/service/tutorial-designer-automobile-price-deploy.md

@@ -1,7 +1,7 @@
 ---
 title: 'Tutorial: Deploy a machine learning model with the designer'
 titleSuffix: Azure Machine Learning
-description: Learn how to build a predictive analytics solution in Azure Machine Learning designer (preview). Train, score, and deploy a machine learning model using drag and drop modules.
+description: Learn how to build a predictive analytics solution in Azure Machine Learning designer (preview). Train, score, and deploy a machine learning model by using drag-and-drop modules.
 
 author: peterclu
 ms.author: peterlu
@@ -15,27 +15,27 @@ ms.date: 11/04/2019
 # Tutorial: Deploy a machine learning model with the designer (preview)
 [!INCLUDE [applies-to-skus](../../../includes/aml-applies-to-enterprise-sku.md)]
 
-You can deploy the predictive model developed in [part one of the tutorial](tutorial-designer-automobile-price-train-score.md) to give others a chance to use it. In part 1, you trained your model. Now, it's time to generate new predictions based on user input. In this part of the tutorial, you will:
+You can deploy the predictive model developed in [part one of the tutorial](tutorial-designer-automobile-price-train-score.md) to give others a chance to use it. In part one, you trained your model. Now, it's time to generate new predictions based on user input. In this part of the tutorial, you will:
 
 > [!div class="checklist"]
-> * Create a real-time inference pipeline
-> * Create an inferencing cluster
-> * Deploy a real-time endpoint
-> * Test a real-time endpoint
+> * Create a real-time inference pipeline.
+> * Create an inferencing cluster.
+> * Deploy the real-time endpoint.
+> * Test the real-time endpoint.
 
 ## Prerequisites
 
 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.
 
 ## Create a real-time inference pipeline
 
-In order to deploy your pipeline, you must first convert the training pipeline into a real-time inference pipeline. This process removes training modules and adds inputs and outputs for inferencing requests.
+To deploy your pipeline, you must first convert the training pipeline into a real-time inference pipeline. This process removes training modules and adds inputs and outputs for inferencing requests.
 
 ### Create a real-time inference pipeline
 
-1. Above the pipeline canvas, select **Create inference pipeline** > **Real-time inference pipeline**
+1. Above the pipeline canvas, select **Create inference pipeline** > **Real-time inference pipeline**.
 
-    Your pipeline should now look like this:  
+    Your pipeline should now look like this: 
 
    ![Screenshot showing the expected configuration of the pipeline after preparing it for deployment](./media/ui-tutorial-automobile-price-deploy/real-time-inference-pipeline.png)
 
@@ -44,13 +44,13 @@ In order to deploy your pipeline, you must first convert the training pipeline i
     * The trained model is stored as a **Dataset** module in the module palette. You can find it under **My Datasets**.
     * Training modules like **Train Model** and **Split Data** are removed.
     * The saved trained model is added back into the pipeline.
-    * **Web Service Input** and **Web Service Output** modules are added. These modules show where user data will enter the model, and where data is returned.
+    * **Web Service Input** and **Web Service Output** modules are added. These modules show where user data enters the model and where data is returned.
 
-    > [!Note]
-    > The **training pipeline** is saved under the new tab at the top of the pipeline canvas. It can also be found as a published pipeline in the designer.
+    > [!NOTE]
+    > The *training pipeline* is saved under the new tab at the top of the pipeline canvas. It can also be found as a published pipeline in the designer.
     >
 
-1. Select **Run** and use the same compute target and experiment you used in part 1.
+1. Select **Run**, and use the same compute target and experiment that you used in part one.
 
 1. Select the **Score Model** module.
 
@@ -60,24 +60,24 @@ In order to deploy your pipeline, you must first convert the training pipeline i
 
 ## Create an inferencing cluster
 
-In the dialog that appears, you can select from any existing Azure Kubernetes Service (AKS) clusters in to deploy your model to. If you don't have an AKS cluster, use the following steps to create one.
+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. Select **Compute** in the dialog box that appears to navigate to the **Compute** page.
+1. Select **Compute** in the dialog box that appears to go to the **Compute** page.
 
-1. In the navigation ribbon, select **Inference Clusters** > **+ New**.
+1. On the navigation ribbon, select **Inference Clusters** > **+ New**.
 
-    ![Screenshot showing how to navigate to the new inference cluster pane](./media/ui-tutorial-automobile-price-deploy/new-inference-cluster.png)
+    ![Screenshot showing how to get to the new inference cluster pane](./media/ui-tutorial-automobile-price-deploy/new-inference-cluster.png)
 
 1. In the inference cluster pane, configure a new Kubernetes Service.
 
-1. Enter "aks-compute" for the **Compute name**.
+1. Enter *aks-compute* for the **Compute name**.
 
-1. Select a nearby available **Region**.
+1. Select a nearby region that's available for the **Region**.
 
 1. Select **Create**.
 
-    > [!Note]
-    > It takes approximately 15 minutes to create a new AKS service. You can check the provisioning state on the **Inference Clusters** page
+    > [!NOTE]
+    > It takes approximately 15 minutes to create a new AKS service. You can check the provisioning state on the **Inference Clusters** page.
     >
 
 ## Deploy the real-time endpoint
@@ -94,21 +94,21 @@ After your AKS service has finished provisioning, return to the real-time infere
 
     ![Screenshot showing how to set up a new real-time endpoint](./media/ui-tutorial-automobile-price-deploy/setup-endpoint.png)
 
-    A success notification above the canvas will appear when deployment completes, it may take a few minutes.
+    A success notification above the canvas appears after deployment finishes. It might take a few minutes.
 
 ## Test the real-time endpoint
 
-Once deployment completes, you can test your real-time endpoint by navigating to the **Endpoints** page.
+After deployment finishes, you can test your real-time endpoint by going to the **Endpoints** page.
 
 1. On the **Endpoints** page, select the endpoint you deployed.
 
     ![Screenshot showing the real-time endpoints tab with the recently created endpoint highlighted](./media/ui-tutorial-automobile-price-deploy/endpoints.png)
 
 1. Select **Test**.
 
-1. You can manually input testing data or use the autofilled sample data and select **Test**.
+1. You can manually input testing data or use the autofilled sample data, and select **Test**.
 
-    The portal submits a test request to the endpoint and shows the results. Although a price value is generated for the input data, it is not used to generate the prediction value.
+    The portal submits a test request to the endpoint and shows the results. Although a price value is generated for the input data, it isn't used to generate the prediction value.
 
     ![Screenshot showing how to test the real-time endpoint with the scored label for price highlighted](./media/ui-tutorial-automobile-price-deploy/test-endpoint.png)
 
@@ -118,7 +118,7 @@ Once deployment completes, you can test your real-time endpoint by navigating to
 
 ## Next steps
 
-In this tutorial, you learned the key steps in creating, deploying, and consuming a machine learning model in the designer. To learn more about how you can use the designer to solve other types of problems, see out our other sample pipelines.
+In this tutorial, you learned the key steps in how to create, deploy, and consume a machine learning model in the designer. To learn more about how you can use the designer to solve other types of problems, see our other sample pipelines.
 
 > [!div class="nextstepaction"]
 > [Credit risk classification sample](how-to-designer-sample-classification-credit-risk-cost-sensitive.md)