fix Acrolinx

Author: shohei1029

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

@@ -133,7 +133,7 @@ The following steps explain how the Azure Machine Learning inference HTTP server
 There are two ways to use Visual Studio Code (VSCode) 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. 
 
 1. User starts the AzureML Inference Server in a command line and use VSCode + Python Extension to attach to the process.
-1. User sets up the `launch.json` in the VSCode and start the AzureML Inference Server within VSCode.
+1. User sets up the `launch.json` in the VSCode and starts the AzureML Inference Server within VSCode.
 
 **launch.json**
 ```json
@@ -167,13 +167,12 @@ TypeError: register() takes 3 positional arguments but 4 were given
 
 ```
 
-You have **Flask 2** installed in your python environment but are running a version of `azureml-inference-server-http`
-that does not support Flask 2. Support for Flask 2 is added in `azureml-inference-server-http>=0.7.0`, which is also in `azureml-defaults>=1.44`.
+You have **Flask 2** installed in your python environment but are running a version of `azureml-inference-server-http` that doesn't support Flask 2. Support for Flask 2 is added in `azureml-inference-server-http>=0.7.0`, which is also in `azureml-defaults>=1.44`.
 
-1. If you are not using this package in an AzureML docker image, please use the latest version of
+1. If you're not using this package in an AzureML docker image, use the latest version of
    `azureml-inference-server-http` or `azureml-defaults`.
 
-2. If you are using this package with an AzureML docker image, please make sure your are using an image built in or after July,
+2. If you're using this package with an AzureML docker image, make sure you're using an image built in or after July,
    2022. The image version is available in the container logs. You should be able to find a log similar to below:
 
     ```
@@ -186,32 +185,21 @@ that does not support Flask 2. Support for Flask 2 is added in `azureml-inferenc
     2022-08-22T17:05:02,190557998+00:00 | gunicorn/run | 
     ```
 
-   The build date of the image appears after "Materialization Build", which in the above example is `20220708`, or July
-   8th, 2022. This is an image compatible with Flask 2. If you don't see a banner like this in your container log, your
-   image is out-of-date and should be updated. If you are using a cuda image, and are unable to find a newer image,
-   please check if your image is deprecated in [AzureML-Containers](https://github.com/Azure/AzureML-Containers). If it
-   is, you should be able to find replacements.
-
-   If this is an online endpoint, you can also find the logs under "Deployment logs" in the [online endpoint page in
-   Azure Machine Learning Studio](https://ml.azure.com/endpoints). If you deploy with SDK v1 and do not explicitly
-   specify an image in your deployment configuration, it will default to using a version of `openmpi4.1.0-ubuntu20.04`
-   that matches your local SDK toolset, which may not be the latest version of the image. For example, SDK 1.43 will
-   default to using `openmpi4.1.0-ubuntu20.04:20220616`, which is incompatible. Please make sure you use the latest SDK
-   for your deployment.
-
-   If for some reason you're unable to update the image, you can temporary workaround the issue by pinning
-   `azureml-defaults==1.43` or `azureml-inference-server-http~=0.4.13`, which will install the older version server with
-   `Flask 1.0.x`.
+   The build date of the image appears after "Materialization Build", which in the above example is `20220708`, or July 8, 2022. This image is compatible with Flask 2. If you don't see a banner like this in your container log, your image is out-of-date, and should be updated. If you're using a cuda image, and are unable to find a newer image, check if your image is deprecated in [AzureML-Containers](https://github.com/Azure/AzureML-Containers). If it is, you should be able to find replacements.
+
+   If this is an online endpoint, you can also find the logs under "Deployment logs" in the [online endpoint page in Azure Machine Learning studio](https://ml.azure.com/endpoints). If you deploy with SDK v1 and don't explicitly specify an image in your deployment configuration, it will default to using a version of `openmpi4.1.0-ubuntu20.04` that matches your local SDK toolset, which may not be the latest version of the image. For example, SDK 1.43 will default to using `openmpi4.1.0-ubuntu20.04:20220616`, which is incompatible. Make sure you use the latest SDK for your deployment.
+
+   If for some reason you're unable to update the image, you can temporarily avoid the issue by pinning `azureml-defaults==1.43` or `azureml-inference-server-http~=0.4.13`, which will install the older version server with `Flask 1.0.x`.
    
    See also [Troubleshooting online endpoints deployment](how-to-troubleshoot-online-endpoints.md#error-resourcenotready).
 
-### 2. I encountered an ``ImportError`` or ``ModuleNotFoundError`` on modules ``opencensus``, ``jinja2``, ``MarkupSafe``, or ``click`` during startup like the following:
+### 2. I encountered an ``ImportError`` or ``ModuleNotFoundError`` on modules ``opencensus``, ``jinja2``, ``MarkupSafe``, or ``click`` during startup like the following message:
 
 ```bash
 ImportError: cannot import name 'Markup' from 'jinja2'
 ```
 
-Older versions (<= 0.4.10) of the server did not pin Flask's dependency to compatible versions. This is fixed in the latest version of the server.
+Older versions (<= 0.4.10) of the server didn't pin Flask's dependency to compatible versions. This problem is fixed in the latest version of the server.
 
 ### 3. Do I need to reload the server when changing the score script?