edit pass: five-machine-learning-articles

Author: ShawnJackson

articles/machine-learning/how-to-retrain-designer.md

@@ -27,105 +27,94 @@ In this article, you learn how to:
 ## Prerequisites
 
 * An Azure subscription. If you don’t have an Azure subscription, create a [free account](https://aka.ms/AMLFree).
-
 * An Azure Machine Learning workspace with the Enterprise SKU.
 
 This article assumes that you have basic knowledge of building pipelines in the designer. For a guided introduction to the designer, complete the [tutorial](tutorial-designer-automobile-price-train-score.md). 
 
 ### Sample pipeline
 
-The pipeline used in this article is an altered version of the one found in [Sample 3 - Income Prediction](how-to-designer-sample-classification-predict-income.md). It uses the [Import Data](algorithm-module-reference/import-data.md) module instead of the sample dataset to show you how to train a model using your own data.
+The pipeline used in this article is an altered version of the one found in [Sample 3: Income prediction](how-to-designer-sample-classification-predict-income.md). It uses the [Import Data](algorithm-module-reference/import-data.md) module instead of the sample dataset to show you how to train a model by using your own data.
 
-![Screenshot showing the modified sample pipeline with a box highlighting the Import Data module](./media/how-to-retrain-designer/modified-sample-pipeline.png)
+![Screenshot that shows the modified sample pipeline with a box highlighting the Import Data module](./media/how-to-retrain-designer/modified-sample-pipeline.png)
 
 ## Train a machine learning model
 
-To retrain a model, you need an initial model. In this section, you learn how to train a model and access the saved model using the designer.
+To retrain a model, you need an initial model. In this section, you learn how to train a model and access the saved model by using the designer.
 
 1. Select the **Import Data** module.
-1. In the properties pane, specify a data source.
-
-    ![Screenshot showing a sample configuration of the Import Data module](./media/how-to-retrain-designer/import-data-settings.png)
+1. On the properties pane, specify a data source.
 
-    For this example, the data is stored in an [Azure datastore](how-to-access-data.md). If you don't already have a datastore, you can create one now by selecting **New datastore**.
+   ![Screenshot that shows a sample configuration of the Import Data module](./media/how-to-retrain-designer/import-data-settings.png)
 
-1. Specify the path to your data. You can also select **Browse path** to visually navigate your datastore. 
+   For this example, the data is stored in an [Azure datastore](how-to-access-data.md). If you don't already have a datastore, you can create one now by selecting **New datastore**.
 
+1. Specify the path to your data. You can also select **Browse path** to browse to your datastore. 
 1. Select **Run** at the top of the canvas.
 
-    > [!NOTE]
-    > If you have already set a default compute for this pipeline draft, the pipeline will run automatically. Otherwise, you can follow the prompts in the settings pane that appears to set one now.
+   > [!NOTE]
+   > If you have already set a default compute for this pipeline draft, the pipeline will run automatically. Otherwise, you can follow the prompts on the settings pane to set one now.
 
-### Locate your trained model
+### Find your trained model
 
 The designer saves all pipeline outputs, including trained models, to the default storage account. You can also access the trained model directly in the designer:
 
 1. Wait for the pipeline to finish running.
-
 1. Select the **Train Model** module.
+1. On the settings pane, select **Outputs+logs**.
+1. Select the **View output** icon, and follow the instruction in the pop-up window to find the trained model.
 
-1. In the settings pane, select **Outputs+logs**.
-
-1. Click on **View output** icon, and follow the instruction in the pop-up window to locate the trained model.
-
-![Screenshot showing how to download the trained model](./media/how-to-retrain-designer/trained-model-view-output.png)
+![Screenshot that shows how to download the trained model](./media/how-to-retrain-designer/trained-model-view-output.png)
 
 ## Create a pipeline parameter
 
 Add pipeline parameters to dynamically set variables at runtime. For this pipeline, add a parameter for the training data path so that you can retrain your model on a new dataset.
 
 1. Select the **Import Data** module.
-1. In the settings pane, select the ellipses above the **Path** field.
+1. On the settings pane, select the ellipses above the **Path** field.
 1. Select  **Add to pipeline parameter**.
-1. Provide a parameter name and default value.
+1. Provide a parameter name and a default value.
 
-    > [!NOTE]
-    > You can inspect and edit your pipeline parameters by selecting the **Settings gear icon** next to the title of your pipeline draft. 
+   > [!NOTE]
+   > You can inspect and edit your pipeline parameters by selecting the **Settings** gear icon next to the title of your pipeline draft. 
 
-![Screenshot showing how to create a pipeline parameter](media/how-to-retrain-designer/add-pipeline-parameter.png)
+![Screenshot that shows how to create a pipeline parameter](media/how-to-retrain-designer/add-pipeline-parameter.png)
 
 ## Publish a training pipeline
 
-When you publish a pipeline, it creates a pipeline endpoint. Pipeline endpoints let you reuse and manage your pipelines for repeatability and automation. In this example, you have setup your pipeline for retraining.
+When you publish a pipeline, it creates a pipeline endpoint. Pipeline endpoints let you reuse and manage your pipelines for repeatability and automation. In this example, you have set up your pipeline for retraining.
 
 1. Select **Publish** above the designer canvas.
-1. Select, or create, a new pipeline endpoint.
+1. Select or create a pipeline endpoint.
 
-    > [!NOTE]
-    > You can publish multiple pipelines to a single endpoint. Each pipeline in the endpoint is given a version number, which you can specify when you call the pipeline endpoint.
+   > [!NOTE]
+   > You can publish multiple pipelines to a single endpoint. Each pipeline in the endpoint is given a version number, which you can specify when you call the pipeline endpoint.
 
 1. Select **Publish**.
 
 ## Retrain your model
 
-Now that you have a published training pipeline, you can use it to retrain your model using new data. You can submit runs from a pipeline endpoint from either portal or programatically.
+Now that you have a published training pipeline, you can use it to retrain your model by using new data. You can submit runs from a pipeline endpoint from the Azure portal or submit them programmatically.
 
-### Submit runs with the designer
+### Submit runs by using the designer
 
 Use the following steps to submit a pipeline endpoint run from the designer:
 
 1. Go to the **Endpoints** page.
-
 1. Select the **Pipeline endpoints** tab.
-
 1. Select your pipeline endpoint.
-
 1. Select the **Published pipelines** tab.
-
-1. Select the pipeline you want to run.
-
+1. Select the pipeline that you want to run.
 1. Select **Submit**.
+1. In the setup dialog box, you can specify a new value for the input data path value. This value points to your new dataset.
 
-1. In the setup dialog, you can specify a new input data path value, which points to your new dataset.
-
-![Screenshot showing how to set up a parameterized pipeline run in the designer](./media/how-to-retrain-designer/published-pipeline-run.png)
+![Screenshot that shows how to set up a parameterized pipeline run in the designer](./media/how-to-retrain-designer/published-pipeline-run.png)
 
-### Submit runs with code
+### Submit runs by using code
 
 You can find the REST endpoint of a published pipeline in the overview panel. By calling the endpoint, you can retrain the published pipeline.
 
-To make a REST call, you will 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. For information about setting up authentication to your workspace and making a parameterized REST call, see [Build an Azure Machine Learning pipeline for batch scoring](tutorial-pipeline-batch-scoring-classification.md#publish-and-run-from-a-rest-endpoint).
 
 ## Next steps
 
-Follow the designer [tutorial](tutorial-designer-automobile-price-train-score.md) to train and deploy a regression model.
+Follow the [designer tutorial](tutorial-designer-automobile-price-train-score.md) to train and deploy a regression model.