Merge pull request #110477 from PeterCLu/plu-retrain-update

Plu retrain update

Author: ShannonLeavitt

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

@@ -8,104 +8,106 @@ ms.subservice: core
 ms.topic: how-to
 ms.author: keli19
 author: likebupt
-ms.date: 02/24/2020
+ms.date: 04/06/2020
 ---
 
 # Retrain models with Azure Machine Learning designer (preview)
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-enterprise-sku.md)]
 
-In this how-to article, you learn how to use Azure Machine Learning designer to retrain a machine learning model. Find out how to use published pipelines to automate machine learning workflows for retraining.
+In this how-to article, you learn how to use Azure Machine Learning designer to retrain a machine learning model. You will use published pipelines to automate your workflow and set parameters to train your model on new data. 
 
 In this article, you learn how to:
 
 > [!div class="checklist"]
 > * Train a machine learning model.
 > * Create a pipeline parameter.
 > * Publish your training pipeline.
-> * Retrain your model.
+> * Retrain your model with new parameters.
 
 ## 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.
+* A dataset accessible to the designer. This can be one of the following:
+   * An Azure Machine Learning registered dataset
+    
+     **-or-**
+   * A data file  stored in an Azure Machine Learning datastore.
+   
+For information on data access using the designer see [How to import data into the designer](how-to-designer-import-data.md).
 
-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). 
+This article also assumes that you have basic knowledge of building pipelines in the designer. For a guided introduction, 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 by using your own data.
+The pipeline used in this article is an altered version of [Sample 3: Income prediction](samples-designer.md#classification-samples). The pipeline uses the [Import Data](algorithm-module-reference/import-data.md) module instead of the sample dataset to show you how to train models using your own data.
 
 ![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
+## Create a pipeline parameter
 
-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.
+Create pipeline parameters to dynamically set variables at runtime. For this example, you will change the training data path from a fixed value to a parameter, so that you can retrain your model on different data.
 
 1. Select the **Import Data** module.
-1. On the properties pane, specify a data source.
-
-   ![Screenshot that shows a sample configuration of the Import Data module](./media/how-to-retrain-designer/import-data-settings.png)
 
-   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**.
+    > [!NOTE]
+    > This example uses the Import Data module to access data in a registered datastore. However, you can follow similar steps if you use alternative data access patterns.
 
-1. Specify the path to your data. You can also select **Browse path** to browse to your datastore. 
-1. Select **Submit** 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 on the settings pane to set one now.
+1. In the module detail pane, to the right of the canvas, select your data source.
 
-### Find your trained model
+1. Enter the path to your data. You can also select **Browse path** to browse your file tree. 
 
-The designer saves all pipeline outputs, including trained models, to the default storage account. However, you can also access trained models directly in the designer:
+1. Mouseover the **Path** field, and select the ellipses above the **Path** field that appear.
 
-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.
-
-![Screenshot that shows how to download the trained model](./media/how-to-retrain-designer/trained-model-view-output.png)
-
-## Create a pipeline parameter
+    ![Screenshot that shows how to create a pipeline parameter](media/how-to-retrain-designer/add-pipeline-parameter.png)
 
-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 **Add to pipeline parameter**.
 
-1. Select the **Import Data** module.
-1. In the settings pane, select the ellipses above the **Path** field.
-1. Select  **Add to pipeline parameter**.
 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. 
 
-![Screenshot that shows how to create a pipeline parameter](media/how-to-retrain-designer/add-pipeline-parameter.png)
+1. Select **Save**.
+
+1. Submit the pipeline run.
+
+## Find a trained model
+
+The designer saves all pipeline output, including trained models, to the default workspace storage account. You can also access trained models directly in the designer:
+
+1. Wait for the pipeline to finish running.
+1. Select the **Train Model** module.
+1. In the module details pane, to the right of the canvas, select **Outputs + logs**.
+1. You can find your model in **Other outputs** along with run logs.
+1. Alternatively, select the **View output** icon. From here, you can follow the instruction in the dialog to navigate directly to your datastore. 
+
+![Screenshot that shows how to download the trained model](./media/how-to-retrain-designer/trained-model-view-output.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 set up your pipeline for retraining.
+Publish a pipeline to a pipeline endpoint to easily reuse your pipelines in the future. A pipeline endpoint creates a REST endpoint to invoke pipeline in the future. In this example, your pipeline endpoint lets you reuse your pipeline to retrain a model on different data.
 
 1. Select **Publish** above the designer canvas.
 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.
+   > You can publish multiple pipelines to a single endpoint. Each pipeline in a given 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 by using new data. You can submit runs from a pipeline endpoint from the Azure portal or submit them programmatically.
+Now that you have a published training pipeline, you can use it to retrain your model on new data. You can submit runs from a pipeline endpoint from the studio workspace or programmatically.
 
 ### Submit runs by using the designer
 
-Use the following steps to submit a pipeline endpoint run from the designer:
+Use the following steps to submit a parameterized 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 that you want to run.
+1. Go to the **Endpoints** page in your studio workspace.
+1. Select the **Pipeline endpoints** tab. Then, select your pipeline endpoint.
+1. Select the **Published pipelines** tab. Then, select the pipeline version 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 box, you can specify the parameters values for the run. For this example, update the data path to train your model using a non-US dataset.
 
 ![Screenshot that shows how to set up a parameterized pipeline run in the designer](./media/how-to-retrain-designer/published-pipeline-run.png)
 
@@ -117,4 +119,6 @@ To make a REST call, you need an OAuth 2.0 bearer-type authentication header. Fo
 
 ## Next steps
 
-Follow the [designer tutorial](tutorial-designer-automobile-price-train-score.md) to train and deploy a regression model.
+In this article, you learned how to create a parameterized training pipeline endpoint using the designer.
+
+For a complete walkthrough of how you can deploy a model to make predictions, see the [designer tutorial](tutorial-designer-automobile-price-train-score.md) to train and deploy a regression model.