Merge pull request #221113 from shohei1029/patch-7

embed score.py instead of just linking it

Author: prmerger-automator[bot]

articles/machine-learning/how-to-deploy-online-endpoints.md

@@ -62,7 +62,7 @@ The main example in this doc uses managed online endpoints for deployment. To us
 # [ARM template](#tab/arm)
 
 > [!NOTE]
-> While the Azure CLI and CLI extension for machine learning are used in these steps, they are not the main focus. They are used more as utilities, passing templates to Azure and checking the status of template deployments.
+> While the Azure CLI and CLI extension for machine learning are used in these steps, they're not the main focus. they're used more as utilities, passing templates to Azure and checking the status of template deployments.
 
 [!INCLUDE [basic prereqs cli](../../includes/machine-learning-cli-prereqs.md)]
 
@@ -354,7 +354,7 @@ For more information on creating an environment, see
 
     :::code language="azurecli" source="~/azureml-examples-main/deploy-arm-templates-az-cli.sh" id="create_model":::
 
-1. Part of the environment is a conda file that specifies the model dependencies needed to host the model. The following example demonstrates how to read the contents of the conda file into an environment variables:
+1. Part of the environment is a conda file that specifies the model dependencies needed to host the model. The following example demonstrates how to read the contents of the conda file into environment variables:
 
     :::code language="azurecli" source="~/azureml-examples-main/deploy-arm-templates-az-cli.sh" id="read_condafile":::
 
@@ -375,35 +375,39 @@ For supported general-purpose and GPU instance types, see [Managed online endpoi
 
 ### Use more than one model
 
-Currently, you can specify only one model per deployment in the YAML. If you've more than one model, when you register the model, copy all the models as files or subdirectories into a folder that you use for registration. In your scoring script, use the environment variable `AZUREML_MODEL_DIR` to get the path to the model root folder. The underlying directory structure is retained. For an example of deploying multiple models to one deployment, see [Deploy multiple models to one deployment](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/minimal/multimodel/README.md).
+Currently, you can specify only one model per deployment in the YAML. If you've more than one model, when you register the model, copy all the models as files or subdirectories into a folder that you use for registration. In your scoring script, use the environment variable `AZUREML_MODEL_DIR` to get the path to the model root folder. The underlying directory structure is retained. For an example of deploying multiple models to one deployment, see [Deploy multiple models to one deployment](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/minimal/multimodel).
 
 ## Understand the scoring script
 
 > [!TIP]
 > The format of the scoring script for online endpoints is the same format that's used in the preceding version of the CLI and in the Python SDK.
 
 # [Azure CLI](#tab/azure-cli)
-As noted earlier, the script specified in `code_configuration.scoring_script` must have an `init()` function and a `run()` function. This example uses the [score.py file](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/model-1/onlinescoring/score.py). 
+As noted earlier, the script specified in `code_configuration.scoring_script` must have an `init()` function and a `run()` function. 
 
 # [Python](#tab/python)
-As noted earlier, the script specified in `CodeConfiguration(scoring_script="score.py")` must have an `init()` function and a `run()` function. This example uses the [score.py file](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/model-1/onlinescoring/score.py). 
+As noted earlier, the script specified in `CodeConfiguration(scoring_script="score.py")` must have an `init()` function and a `run()` function. 
 
 # [ARM template](#tab/arm)
 
 As noted earlier, the script specified in `code_configuration.scoring_script` must have an `init()` function and a `run()` function. This example uses the [score.py file](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/model-1/onlinescoring/score.py). 
 
-When using a template for deployment, you must first upload the scoring file(s) to an Azure Blob store and then register it:
+When using a template for deployment, you must first upload the scoring file(s) to an Azure Blob store, and then register it:
 
 1. The following example uses the Azure CLI command `az storage blob upload-batch` to upload the scoring file(s):
 
     :::code language="azurecli" source="~/azureml-examples-main/deploy-arm-templates-az-cli.sh" id="upload_code":::
 
-1. The following example demonstrates hwo to register the code using a template:
+1. The following example demonstrates how to register the code using a template:
 
     :::code language="azurecli" source="~/azureml-examples-main/deploy-arm-templates-az-cli.sh" id="create_code":::
 
 ---
 
+This example uses the [score.py file](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/model-1/onlinescoring/score.py):
+__score.py__
+:::code language="python" source="~/azureml-examples-main/cli/endpoints/online/model-1/onlinescoring/score.py" :::
+
 The `init()` function is called when the container is initialized or started. Initialization typically occurs shortly after the deployment is created or updated. Write logic here for global initialization operations like caching the model in memory (as we do in this example). The `run()` function is called for every invocation of the endpoint and should do the actual scoring and prediction. In the example, we extract the data from the JSON input, call the scikit-learn model's `predict()` method, and then return the result.
 
 ## Deploy and debug locally by using local endpoints