edit pass: automobile-price-train-score-and-deploy

Author: paulth1

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)

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

@@ -136,7 +136,7 @@ Your dataset still has missing values after you remove the **normalized-losses**
 
 1. In the properties pane, select **Remove entire row** under **Cleaning mode**.
 
-1. In the properties pane **Comment** box, enter *Remove missing value rows*.  
+1. In the properties pane **Comment** box, enter *Remove missing value rows*. 
 
     Your pipeline should now look something like this:
 

includes/aml-create-in-portal-enterprise.md

@@ -13,7 +13,7 @@ ms.date: 10/04/2019
 
 1. Sign in to the [Azure portal](https://portal.azure.com/) by using the credentials for your Azure subscription. 
 
-1. In the upper-left corner of Azure portal, select **+ Create a resource**.
+1. In the upper-left corner of the Azure portal, select **+ Create a resource**.
 
       ![Create a new resource](media/aml-create-in-portal/create-workspace.gif)
 
@@ -27,15 +27,15 @@ ms.date: 10/04/2019
 
    Field|Description 
    ---|---
-   Workspace name |Enter a unique name that identifies your workspace. In this example, we use **docs-ws**. Names must be unique across the resource group. Use a name that's easy to recall and to differentiate from workspaces created by others.  
+   Workspace name |Enter a unique name that identifies your workspace. In this example, we use **docs-ws**. Names must be unique across the resource group. Use a name that's easy to recall and to differentiate from workspaces created by others. 
    Subscription |Select the Azure subscription that you want to use.
-   Resource group | Use an existing resource group in your subscription or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. In this example, we use **docs-aml**. 
+   Resource group | Use an existing resource group in your subscription, or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. In this example, we use **docs-aml**. 
    Location | Select the location closest to your users and the data resources to create your workspace.
-   Workspace edition | Select  **Enterprise**.  This tutorial requires the use of the Enterprise edition.  The Enterprise edition is in preview and does not currently add any extra costs.  
+   Workspace edition | Select **Enterprise**. This tutorial requires the use of the Enterprise edition. The Enterprise edition is in preview and doesn't currently add any extra costs. 
 
-1. After you are finished configuring the workspace, select **Create**. 
+1. After you're finished configuring the workspace, select **Create**. 
 
-   > [!Warning] 
+   > [!WARNING] 
    > It can take several minutes to create your workspace in the cloud.
 
    When the process is finished, a deployment success message appears. 

includes/aml-ui-cleanup.md

@@ -15,7 +15,7 @@ ms.date: 11/06/2019
 
 ### Delete everything
 
-If you don't plan to use anything that you created, delete the entire resource group so you don't incur any charges:
+If you don't plan to use anything that you created, delete the entire resource group so you don't incur any charges.
 
 1. In the Azure portal, select **Resource groups** on the left side of the window.
 
@@ -25,20 +25,20 @@ If you don't plan to use anything that you created, delete the entire resource g
 
 1. Select **Delete resource group**.
 
-Deleting the resource group also deletes all resources that you created in the designer.  
+Deleting the resource group also deletes all resources that you created in the designer. 
 
 ### Delete individual assets
 
 In the designer where you created your experiment, delete individual assets by selecting them and then selecting the **Delete** button.
 
-The compute target that you created here *automatically autoscales* to zero nodes when it's not being used. This is to minimize charges. If you want to delete the compute target, take these steps:
+The compute target that you created here *automatically autoscales* to zero nodes when it's not being used. This action is taken to minimize charges. If you want to delete the compute target, take these steps:
 
 ![Delete assets](./media/aml-ui-cleanup/delete-asset.png)
 
 Datasets can be unregistered from your workspace by selecting each dataset and selecting **Unregister**.
 
 ![Unregister dataset](./media/aml-ui-cleanup/unregister-dataset.png)
 
-To delete a dataset, navigate to the storage account using the Azure portal or the Storage Explorer and manually delete those assets.
+To delete a dataset, go to the storage account by using the Azure portal or the Storage Explorer and manually delete those assets.
 
 

includes/aml-ui-create-training-compute.md

@@ -11,7 +11,7 @@ ms.topic: "include"
 ms.date: 10/09/2019
 ---
 
-A pipeline runs on a compute target which is a compute resource that is attached to your workspace. Once you create a compute target, you can reuse it for future runs.
+A pipeline runs on a compute target, which is a compute resource that's attached to your workspace. After you create a compute target, you can reuse it for future runs.
 
 1. Select **Run** at the top of the canvas to run the pipeline.
 
@@ -20,28 +20,28 @@ A pipeline runs on a compute target which is a compute resource that is attached
     If you already have an available compute target, you can select it to run this pipeline.
 
     > [!NOTE]
-    > The designer can only run experiments on Machine Learning Compute targets. Other compute targets will not be shown.
+    > The designer can run experiments only on Azure Machine Learning Compute targets. Other compute targets won't be shown.
 
-1. Provide a name for the compute resource.
+1. Enter a name for the compute resource.
 
 1. Select **Save**.
 
     ![Setup compute target](./media/aml-ui-create-training-compute/set-compute.png)
 
 1. Select **Run**.
 
-1. In the **Set up pipeline run** dialog, select **+ New experiment** for the **Experiment**
+1. In the **Set up pipeline run** dialog box, select **+ New experiment** for the **Experiment**.
 
     > [!NOTE]
     > Experiments group similar pipeline runs together. If you run a pipeline multiple times, you can select the same experiment for successive runs.
 
-    * Enter a descriptive **Experiment Name**
+    1. Enter a descriptive name for **Experiment Name**.
 
-    * Select **Run**
+    1. Select **Run**.
 
     You can view run status and details at the top right of the canvas.
 
     > [!NOTE]
-    > It takes approximately 5 minutes to create a compute resource. After the resource is created, you can reuse it and skip this wait time for future runs.
+    > It takes approximately five minutes to create a compute resource. After the resource is created, you can reuse it and skip this wait time for future runs.
     >
-    > The compute resource will autoscale to 0 nodes when it is idle to save cost.  When you use it again after a delay, you may again experience approximately 5 minutes of wait time while it scales back up.
+    > The compute resource autoscales to zero nodes when it's idle to save cost. When you use it again after a delay, you might experience approximately five minutes of wait time while it scales back up.