Add perf changes

Author: PeterCLu

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

@@ -54,7 +54,7 @@ To deploy your pipeline, you must first convert the training pipeline into a rea
 
 1. Select **Submit**, and use the same compute target and experiment that you used in part one.
 
-    If is the first run, it may take up to 20 minutes for your pipeline to finish running. The default compute settings have a minimum node size of 0 which means that the designer must allocate resources after being idle for some time. Repeated submits will take less time since the compute resources are already allocated. Future runs will take significantly less time because the designer will use cached results for each module unless you specify otherwise with the **Regenerate output** option. 
+    If is the first run, it may take up to 20 minutes for your pipeline to finish running. The default compute settings have a minimum node size of 0, which means that the designer must allocate resources after being idle. Repeated pipeline runs will take less time since the compute resources are already allocated. Additionally, the designer uses cached results for each module to further improve efficiency.
 
 1. Select **Deploy**.
 

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

@@ -99,7 +99,7 @@ You can visualize the data to understand the dataset that you'll use.
 
 1. Select the **Automobile price data (Raw)** module.
 
-1. In the module details pane to the right of the canvas, select **Outputs**.
+1. In the module details pane to the right of the canvas, select **Outputs + log**.
 
 1. Select the graph icon to visualize the data.
 
@@ -280,15 +280,15 @@ Now that your pipeline is all setup, you can submit a pipeline run to train your
 
     You can view run status and details at the top right of the canvas.
 
-    If is the first run, it may take up to 20 minutes for your pipeline to finish running. The default compute settings have a minimum node size of 0 which means that the designer must allocate resources after being idle for some time. Repeated submits will take less time since the compute resources are already allocated. Future runs will take significantly less time because the designer will use cached results for each module unless you specify otherwise with the **Regenerate output** option. 
+    If is the first run, it may take up to 20 minutes for your pipeline to finish running. The default compute settings have a minimum node size of 0, which means that the designer must allocate resources after being idle. Repeated pipeline runs will take less time since the compute resources are already allocated. Additionally, the designer uses cached results for each module to further improve efficiency.
 
 ### View scored labels
 
 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 module details pane to the right of the canvas, select **Outputs** > graph icon ![visualize icon](./media/tutorial-designer-automobile-price-train-score/visualize-icon.png) to view results.
+1. In the module details pane to the right of the canvas, select **Outputs + logs** > 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.
 
@@ -300,7 +300,7 @@ Use the **Evaluate Model** to see how well the trained model performed on the te
 
 1. Select the **Evaluate Model** module to view its output.
 
-1. In the module details pane to the right of the canvas, select **Output** > graph icon ![visualize icon](./media/tutorial-designer-automobile-price-train-score/visualize-icon.png) to view results.
+1. In the module details pane to the right of the canvas, select **Outputs + logs** > 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: