Merge pull request #102099 from PeterCLu/plu-tutorial-updates

Update designer tutorial

Author: Jak-MS

articles/machine-learning/media/tutorial-designer-automobile-price-train-score/exclude-column.png

@@ -52,10 +52,6 @@ To deploy your pipeline, you must first convert the training pipeline into a rea
 
 1. Select **Run**, and use the same compute target and experiment that you used in part one.
 
-1. Select the **Score Model** module.
-
-1. In the properties pane, select **Outputs** > **Visualize** to verify the model is still working. You can see the original data is displayed along with the predicted price ("Scored Labels").
-
 1. Select **Deploy**.
 
 ## Create an inferencing cluster

articles/machine-learning/media/tutorial-designer-automobile-price-train-score/launch-designer.png

@@ -15,9 +15,7 @@ ms.date: 11/04/2019
 # Tutorial: Predict automobile price with the designer (preview)
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-enterprise-sku.md)]
 
-In this two-part tutorial, you learn how to use the Azure Machine Learning designer to develop and deploy a predictive analytics solution that predicts the price of any car. 
-
-In part one, you set up your environment, drag modules onto an interactive canvas, and connect them together to create an Azure Machine Learning pipeline.
+In this two-part tutorial, you learn how to use the Azure Machine Learning designer to develop and deploy a predictive analytics solution that predicts the price of any car.
 
 In part one of the tutorial, you'll learn how to:
 
@@ -28,7 +26,7 @@ In part one of the tutorial, you'll learn how to:
 > * Train a machine learning model.
 > * Evaluate a machine learning model.
 
-In [part two](tutorial-designer-automobile-price-deploy.md) of the tutorial, you'll learn how to deploy your predictive model as a real-time inferencing endpoint to predict the price of any car based on technical specifications you send it. 
+In [part two](tutorial-designer-automobile-price-deploy.md) of the tutorial, you'll deploy your model as a real-time inferencing endpoint to predict the price of any car based on technical specifications you send it. 
 
 > [!NOTE]
 >A completed version of this tutorial is available as a sample pipeline.
@@ -37,7 +35,9 @@ In [part two](tutorial-designer-automobile-price-deploy.md) of the tutorial, you
 
 ## Create a new pipeline
 
-Azure Machine Learning pipelines organize multiple, dependent machine learning and data processing steps into a single resource. Pipelines help you organize, manage, and reuse complex machine learning workflows across projects and users. To create an Azure Machine Learning pipeline, you need an Azure Machine Learning workspace. In this section, you learn how to create both these resources.
+Azure Machine Learning pipelines organize multiple machine learning and data processing steps into a single resource. Pipelines let you organize, manage, and reuse complex machine learning workflows across projects and users.
+
+To create an Azure Machine Learning pipeline, you need an Azure Machine Learning workspace. In this section, you learn how to create both these resources.
 
 ### Create a new workspace
 
@@ -55,7 +55,7 @@ If you have an Azure Machine Learning workspace with an Enterprise edition, [ski
 
 1. Select **Easy-to-use prebuilt modules**.
 
-1. Select the default pipeline name **Pipeline-Created-on** at the top of the canvas. Rename it to something meaningful. An example is *Automobile price prediction*. The name doesn't need to be unique.
+1. At the top of the canvas, select the default pipeline name **Pipeline-Created-on**. Rename it to *Automobile price prediction*. The name doesn't need to be unique.
 
 ## Import data
 
@@ -105,7 +105,7 @@ When you train a model, you have to do something about the data that's missing.
 
 1. Select the **Select Columns in Dataset** module.
 
-1. In the properties pane to the right of the canvas, select **Parameters** > **Edit column**.
+1. In the properties pane to the right of the canvas, select **All columns**.
 
 1. Select the **+** to add a new rule.
 
@@ -116,12 +116,12 @@ When you train a model, you have to do something about the data that's missing.
 1. In the lower right, select **Save** to close the column selector.
 
     ![Exclude a column](./media/tutorial-designer-automobile-price-train-score/exclude-column.png)
-        
-    The properties pane shows that the **normalized-losses** column is excluded.
 
 1. Select the **Select Columns in Dataset** module. 
 
-1. In the properties pane, select **Parameters** > **Comment** and enter *Exclude normalized losses*.
+1. In the properties pane, select the **Comment** text box and enter *Exclude normalized losses*.
+
+    Comments will appear on the graph to help you organize your pipeline.
 
 ### Clean missing data
 
@@ -144,31 +144,30 @@ Your dataset still has missing values after you remove the **normalized-losses**
 
 ## Train a machine learning model
 
-Now that the data is processed, you can train a predictive model.
-
-### Select an algorithm
-
-*Classification* and *regression* are two types of supervised machine learning algorithms. Classification predicts an answer from a defined set of categories, such as a color like red, blue, or green. Regression is used to predict a number.
+Now that you have the modules in place to process the data, you can set up the training modules.
 
 Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model.
 
 ### Split the data
 
-Split your data into two separate datasets for training the model and testing it.
+Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed.
+
+1. Enter **split data** in the search box to find the **Split Data** module. Connect the left port of the **Clean Missing Data** module to the **Split Data** module.
 
-1. Enter **split data** in the search box to find the **Split Data** module. Connect it to the left port of the **Clean Missing Data** module.
+    > [!IMPORTANT]
+    > Be sure that the left output ports of **Clean Missing Data** connects to **Split Data**. The left port contains the the cleaned data. The right port contains the discarted data.
 
 1. Select the **Split Data** module.
 
 1. In the properties pane, set the **Fraction of rows in the first output dataset** to 0.7.
 
-    This option splits 70 percent of the data to train the model and 30 percent for testing it.
+    This option splits 70 percent of the data to train the model and 30 percent for testing it. The 70 percent dataset will be accessible through the left output port. The remaining data will be available through the right output port.
 
 1. In the properties pane **Comment** box, enter *Split the dataset into training set (0.7) and test set (0.3)*.
 
 ### Train the model
 
-Train the model by giving it a set of data that includes the price. The model scans through the data and looks for correlations between a car's features and its price to construct a model.
+Train the model by giving it a dataset that includes the price. The algorithm constructs a model that explains the relationship between the features and the price as presented by the training data.
 
 1. To select the learning algorithm, clear your module palette search box.
 
@@ -183,6 +182,9 @@ Train the model by giving it a set of data that includes the price. The model sc
 1. Connect the output of the **Linear Regression** module to the left input of the **Train Model** module.
 
 1. Connect the training data output (left port) of the **Split Data** module to the right input of the **Train Model** module.
+    
+    > [!IMPORTANT]
+    > Be sure that the left output ports of **Split Data** connects to **Train Model**. The left port contains the the training set. The right port contains the test set.
 
     ![Screenshot showing the correct configuration of the Train Model module. The Linear Regression module connects to left port of Train Model module and the Split Data module connects to right port of Train Model](./media/tutorial-designer-automobile-price-train-score/pipeline-train-model.png)
 
@@ -192,20 +194,24 @@ Train the model by giving it a set of data that includes the price. The model sc
 
 1. In the **Label column** dialog box, expand the drop-down menu and select **Column names**. 
 
-1. In the text box, enter *price*. Price is the value that your model is going to predict.
+1. In the text box, enter *price* to specify the value that your model is going to predict.
 
     Your pipeline should look like this:
 
     ![Screenshot showing the correct configuration of the pipeline after adding the Train Model module.](./media/tutorial-designer-automobile-price-train-score/pipeline-train-graph.png)
 
-## Evaluate a machine learning model
+## Score a machine learning model
 
 After you train your model by using 70 percent of the data, you can use it to score the other 30 percent to see how well your model functions.
 
 1. Enter *score model* in the search box to find the **Score Model** module. Drag the module to the pipeline canvas. 
 
 1. Connect the output of the **Train Model** module to the left input port of **Score Model**. Connect the test data output (right port) of the **Split Data** module to the right input port of **Score Model**.
 
+## Evaluate a machine learning model
+
+Use the **Evaluate Model** module to evaluate how well your model scored the test dataset.
+
 1. Enter *evaluate* in the search box to find the **Evaluate Model** module. Drag the module to the pipeline canvas. 
 
 1. Connect the output of the **Score Model** module to the left input of **Evaluate Model**. 
@@ -214,25 +220,29 @@ After you train your model by using 70 percent of the data, you can use it to sc
 
     ![Screenshot showing the correct configuration of the pipeline.](./media/tutorial-designer-automobile-price-train-score/pipeline-final-graph.png)
 
-### Run the pipeline
+## Run the pipeline
 
 [!INCLUDE [aml-ui-create-training-compute](../../includes/aml-ui-create-training-compute.md)]
 
-### View results
+### View scored labels
 
-After the run completes, you can view the results of the pipeline run. 
+After the run completes, you can view the results of the pipeline run. First, look at the predictions generated by the regression model.
 
 1. Select the **Score Model** module to view its output.
 
-1. In the properties pane, select **Outputs** > **Visualize**.
+1. In the properties pane, select **Outputs** > graph icon ![visualize icon](./media/tutorial-designer-automobile-price-train-score/visualize-icon.png) to view results.
 
     Here you can see the predicted prices and the actual prices from the testing data.
 
     ![Screenshot of the output visualization highlighting the Scored Label column](./media/tutorial-designer-automobile-price-train-score/score-result.png)
 
+### Evaluate models
+
+Use the **Evaluate Model** to see how well the trained model performed on the test dataset.
+
 1. Select the **Evaluate Model** module to view its output.
 
-1. In the properties pane, select **Output** > **Visualize**.
+1. In the properties pane, select **Output** > graph icon ![visualize icon](./media/tutorial-designer-automobile-price-train-score/visualize-icon.png) to view results.
 
 The following statistics are shown for your model: