Merge pull request #106625 from PeterCLu/plu-content-verbatims

Address verbatims for the designer and Studio (classic)

Author: v-albemi

articles/machine-learning/algorithm-module-reference/apply-transformation.md

@@ -9,36 +9,40 @@ ms.topic: reference
 
 author: likebupt
 ms.author: keli19
-ms.date: 02/11/2020
+ms.date: 03/05/2020
 ---
 
 # Apply Transformation module
 
 This article describes a module in Azure Machine Learning designer (preview).
 
-Use this module to modify an input dataset based on a previously computed transformation.  
-  
+Use this module to modify an input dataset based on a previously computed transformation.
+
 For example, if you used z-scores to normalize your training data by using the **Normalize Data** module, you would want to use the z-score value that was computed for training during the scoring phase as well. In Azure Machine Learning, you can save the normalization method as a transform, and then using **Apply Transformation** to apply the z-score to the input data before scoring.
-  
-Azure Machine Learning provides support for creating and then applying many different kinds of custom transformations. For example, you might want to save and then reuse transformations to:  
-  
-- Remove or replace missing values, using **Clean Missing Data**
-- Normalize data, using **Normalize Data**
-  
+
+## How to save transformations
+
+The designer lets you save data transformations as **datasets** so that you can use them in other pipelines.
+
+1. Select a data transformation module that has successfully run.
+
+1. Select the **Outputs + logs** tab.
+
+1. Select the **Save icon** to save the **Result Transformation**.
 
 ## How to use Apply Transformation  
 
-1. Add the **Apply Transformation** module to your pipeline. You can find this module in the **Model Scoring & Evaluation** category. 
-  
-2. Locate an existing transformation to use as an input. Previously saved transformations can be found in the **My Datasets** group under **Datasets** category in the left module tree.  
+1. Add the **Apply Transformation** module to your pipeline. You can find this module in the **Model Scoring & Evaluation** section of the module palette. 
 
-   
-  
-3. Connect the dataset that you want to transform. The dataset should have exactly the same schema (number of columns, column names, data types) as the dataset for which the transformation was first designed.  
+1. Find the saved transformation you want to use under **Datasets** > **My Datasets** in the module palette.
+
+1. Connect the output of the saved transformation to the left input port of the **Apply Transformation** module.
+
+    The dataset should have exactly the same schema (number of columns, column names, data types) as the dataset for which the transformation was first designed.  
 
-4. No other parameters need to be set since all customization is done when defining the transformation.  
+1. Connect the dataset output of the desired module to the right input port of the **Apply Transformation** module.
 
-5. To apply a transformation to the new dataset, run the pipeline.  
+1. To apply a transformation to the new dataset, run the pipeline.  
 
 ## Next steps
 

articles/machine-learning/studio/retrain-classic-web-service.md

@@ -44,23 +44,10 @@ You can add scoring endpoints using the sample code provided in this [GitHub rep
 1. Click **Add**.
 1. Type a name and description for the new endpoint. Select the logging level and whether sample data is enabled. For more information on logging, see [Enable logging for Machine Learning web services](web-services-logging.md).
 
-## Update the added endpoint’s trained model
+## Update the added endpoint's trained model
 
 ### Retrieve PATCH URL
 
-### Option 1: Programmatically
-
-To get the correct PATCH URL programmatically, follow these steps:
-
-1. Run the [AddEndpoint](https://github.com/raymondlaghaeian/AML_EndpointMgmt/blob/master/Program.cs) sample code.
-1. From the output of AddEndpoint, find the *HelpLocation* value and copy the URL.
-
-   ![HelpLocation in the output of the addEndpoint sample.](./media/retrain-classic/addEndpoint-output.png)
-1. Paste the URL into a browser to navigate to a page that provides help links for the web service.
-1. Click the **Update Resource** link to open the patch help page.
-
-### Option 2: Use the Azure Machine Learning Web Services portal
-
 Follow these steps to get the correct PATCH URL using the web portal:
 
 1. Sign in to the [Azure Machine Learning Web Services](https://services.azureml.net/) portal.
@@ -96,8 +83,8 @@ The following sample code shows you how to use the *BaseLocation*, *RelativeLoca
                     Location = new AzureBlobDataReference()
                     {
                         BaseLocation = "https://esintussouthsus.blob.core.windows.net/",
-                        RelativeLocation = "your endpoint relative location", //from the output, for example: “experimentoutput/8946abfd-79d6-4438-89a9-3e5d109183/8946abfd-79d6-4438-89a9-3e5d109183.ilearner”
-                        SasBlobToken = "your endpoint SAS blob token" //from the output, for example: “?sv=2013-08-15&sr=c&sig=37lTTfngRwxCcf94%3D&st=2015-01-30T22%3A53%3A06Z&se=2015-01-31T22%3A58%3A06Z&sp=rl”
+                        RelativeLocation = "your endpoint relative location", //from the output, for example: "experimentoutput/8946abfd-79d6-4438-89a9-3e5d109183/8946abfd-79d6-4438-89a9-3e5d109183.ilearner"
+                        SasBlobToken = "your endpoint SAS blob token" //from the output, for example: "?sv=2013-08-15&sr=c&sig=37lTTfngRwxCcf94%3D&st=2015-01-30T22%3A53%3A06Z&se=2015-01-31T22%3A58%3A06Z&sp=rl"
                     }
                 }
             }