update format

Author: likebupt

articles/machine-learning/algorithm-module-reference/toc.yml

@@ -154,7 +154,7 @@
     href: train-anomaly-detection-model.md
 - name: Web Service
   items: 
-  - name: Web Servcie Input/Output
+  - name: Web Service Input/Output
     href: web-service-input-output.md
 - name: Module errors & troubleshooting
   href: designer-error-codes.md

articles/machine-learning/algorithm-module-reference/web-service-input-output.md

@@ -21,29 +21,26 @@ This article describes **Web Service Input** module and **Web Service Output** m
 
 ## How to use Web Service Input/Output
 
-1. When you create a real-time inference pipeline from your training pipeline, **Web Service Input** and **Web Service Output** module will be automatically added to show where user data enters the pipeline and where data is returned. 
+- When you create a real-time inference pipeline from your training pipeline, **Web Service Input** and **Web Service Output** module will be automatically added to show where user data enters the pipeline and where data is returned. 
 
-   Learn more about [create a real-time inference pipeline](https://docs.microsoft.com/azure/machine-learning/tutorial-designer-automobile-price-deploy#create-a-real-time-inference-pipeline).
+    Learn more about [create a real-time inference pipeline](https://docs.microsoft.com/azure/machine-learning/tutorial-designer-automobile-price-deploy#create-a-real-time-inference-pipeline).
 
-   > Note:
-   >
-   > Automatically generating real-time inference pipeline is a rule-based best-effort process, there is no guarantee for the correctness. You can manually add or remove **Web Service Input/Output** modules to satisfy your requirements. Make sure there is at least one **Web Service Input** module and one **Web Service Output** module in your real-time inference pipeline. If you have multiple **Web Service Input** or **Web Service Output** modules, make sure they have unique names, which you can input the name in the right panel of the module.
+    > [!NOTE]
+    > Automatically generating real-time inference pipeline is a rule-based best-effort process, there is no guarantee for the correctness. You can manually add or remove **Web Service Input/Output** modules to satisfy your requirements. Make sure there is at least one **Web Service Input** module and one **Web Service Output** module in your real-time inference pipeline. If you have multiple **Web Service Input** or **Web Service Output** modules, make sure they have unique names, which you can input the name in the right panel of the module.
 
-2. You can also manually create a real-time inference pipeline by adding **Web Service Input** and **Web Service Output** modules to your unsubmitted pipeline.
+- You can also manually create a real-time inference pipeline by adding **Web Service Input** and **Web Service Output** modules to your unsubmitted pipeline.
 
-   > Note:
-   >
-   > The pipeline type will be determined at the first time you submit it. So be sure to add **Web Service Input** and **Web Service Output** module before you submit for the first time if you want to create a real-time inference pipeline.
+    > [!NOTE]
+    >  The pipeline type will be determined at the first time you submit it. So be sure to add **Web Service Input** and **Web Service Output** module before you submit for the first time if you want to create a real-time inference pipeline.
 
    Below example shows how to manually create real-time inference pipeline from **Execute Python Script** module. 
 
    ![Example](media/module/web-service-input-output-example.png)
 
    After you submit the pipeline and the run completes successfully, you will be able to deploy the real-time endpoint.
 
-   > Note:
-   >
-   > In the above example, **Enter Data Manually** provides the data schema for web service input and is necessary for deploying the real-time endpoint. Generally, you should always connect a module or dataset to the port which **Web Service Input** is connected to provide the data schema.
+   > [!NOTE]
+   >  In the above example, **Enter Data Manually** provides the data schema for web service input and is necessary for deploying the real-time endpoint. Generally, you should always connect a module or dataset to the port which **Web Service Input** is connected to provide the data schema.
    
 ## Next steps
 Learn more about [deploy the real-time endpoint](https://docs.microsoft.com/azure/machine-learning/tutorial-designer-automobile-price-deploy#deploy-the-real-time-endpoint).