Merge pull request #226961 from shohei1029/patch-10

move inference HTTP server article into online endpoints

Author: ttorble

articles/machine-learning/how-to-inference-server-http.md

@@ -32,7 +32,7 @@ This article focuses on the Azure Machine Learning inference HTTP server.
 
 The following table provides an overview of scenarios to help you choose what works best for you.
 
-| Scenario                                                                | Inference HTTP Server | Local endpoint |
+| Scenario                                                                | Inference HTTP server | Local endpoint |
 | ----------------------------------------------------------------------- | --------------------- | -------------- |
 | Update local Python environment **without** Docker image rebuild        | Yes                   | No             |
 | Update scoring script                                                   | Yes                   | Yes            |
@@ -130,7 +130,7 @@ Now you can modify the scoring script (`score.py`) and test your changes by runn
 
 There are two ways to use Visual Studio Code (VS Code) and [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) to debug with [azureml-inference-server-http](https://pypi.org/project/azureml-inference-server-http/) package ([Launch and Attach modes](https://code.visualstudio.com/docs/editor/debugging#_launch-versus-attach-configurations)). 
 
--  **Launch mode**: set up the `launch.json` in VS Code and start the AzureML Inference HTTP Server within VS Code.
+-  **Launch mode**: set up the `launch.json` in VS Code and start the AzureML inference HTTP server within VS Code.
    1. Start VS Code and open the folder containing the script (`score.py`).
    1. Add the following configuration to `launch.json` for that workspace in VS Code:
 
@@ -155,7 +155,7 @@ There are two ways to use Visual Studio Code (VS Code) and [Python Extension](ht
 
     1. Start debugging session in VS Code. Select "Run" -> "Start Debugging" (or `F5`).
 
--  **Attach mode**: start the AzureML Inference HTTP Server in a command line and use VS Code + Python Extension to attach to the process.
+-  **Attach mode**: start the AzureML inference HTTP server in a command line and use VS Code + Python Extension to attach to the process.
     > [!NOTE]
     > If you're using Linux environment, first install the `gdb` package by running `sudo apt-get install -y gdb`.
    1. Add the following configuration to `launch.json` for that workspace in VS Code:
@@ -272,7 +272,7 @@ The following steps explain how the Azure Machine Learning inference HTTP server
 
 ## Understanding logs
 
-Here we describe logs of the AzureML Inference HTTP Server. You can get the log when you run the `azureml-inference-server-http` locally, or [get container logs](how-to-troubleshoot-online-endpoints.md#get-container-logs) if you're using online endpoints. 
+Here we describe logs of the AzureML inference HTTP server. You can get the log when you run the `azureml-inference-server-http` locally, or [get container logs](how-to-troubleshoot-online-endpoints.md#get-container-logs) if you're using online endpoints. 
 
 > [!NOTE]
 > The logging format has changed since version 0.8.0. If you find your log in different style, update the `azureml-inference-server-http` package to the latest version.

articles/machine-learning/toc.yml

@@ -611,6 +611,8 @@
               href: how-to-monitor-online-endpoints.md
             - name: Debug online endpoints locally VS Code
               href: how-to-debug-managed-online-endpoints-visual-studio-code.md
+            - name: Debug scoring script with inference HTTP server
+              href: how-to-inference-server-http.md
             - name: Troubleshoot online endpoints
               href: how-to-troubleshoot-online-endpoints.md
         - name: Batch endpoints
@@ -651,9 +653,6 @@
                   href: how-to-use-event-grid-batch.md
                 - name: Use REST to deploy a model as batch endpoints
                   href: how-to-deploy-batch-with-rest.md
-        - name: Inference HTTP server
-          displayName: local debug
-          href: how-to-inference-server-http.md
     - name: Work with MLflow
       items:
         - name: Configure MLflow for Azure Machine Learning