edits

Author: s-polly

articles/machine-learning/tutorial-deploy-model.md

@@ -20,7 +20,8 @@ ms.custom:
 # Deploy a model as an online endpoint
 
 [!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
-Learn to deploy a model to an online endpoint, using Azure Machine Learning Python SDK v2.
+
+Learn to deploy a model to an online endpoint using Azure Machine Learning Python SDK v2.
 
 In this tutorial, you deploy and use a model that predicts the likelihood of a customer defaulting on a credit card payment.
 
@@ -38,7 +39,7 @@ The steps you take are:
 > * Get details of the second deployment
 > * Roll out the new deployment and delete the first one
 
-This video shows how to get started in Azure Machine Learning studio so that you can follow the steps in the tutorial. The video shows how to create a notebook, create a compute instance, and clone the notebook. The steps are also described in the following sections.
+This video shows how to get started in Azure Machine Learning studio so you can follow the steps in the tutorial. The video shows how to create a notebook, create a compute instance, and clone the notebook. The steps are also described in the following sections.
 
 > [!VIDEO https://learn-video.azurefd.net/vod/player?id=7d0e09a5-c319-4e6a-85e2-c9601a0fba68]
 
@@ -57,20 +58,20 @@ This video shows how to get started in Azure Machine Learning studio so that you
 [!INCLUDE [notebook set kernel](includes/prereq-set-kernel.md)] 
 
 > [!NOTE]
->- Serverless Spark Compute doesn't have `Python 3.10 - SDK v2` installed by default. We recommend that users create a compute instance and select it before proceeding with the tutorial.
+> Serverless Spark Compute doesn't have `Python 3.10 - SDK v2` installed by default. We recommend that you create a compute instance and select it before proceeding with the tutorial.
 
 <!-- nbstart https://raw.githubusercontent.com/Azure/azureml-examples/main/tutorials/get-started-notebooks/deploy-model.ipynb -->
 
 
 ## Create handle to workspace
 
-Before you dive in the code, you need a way to reference your workspace. Create `ml_client` for a handle to the workspace and use the `ml_client` to manage resources and jobs.
+Before you dive into the code, you need a way to reference your workspace. Create `ml_client` for a handle to the workspace and use the `ml_client` to manage resources and jobs.
 
 In the next cell, enter your Subscription ID, Resource Group name, and Workspace name. To find these values:
 
 1. In the upper right Azure Machine Learning studio toolbar, select your workspace name.
 1. Copy the value for workspace, resource group, and subscription ID into the code.
-1. You need to copy one value, close the area and paste, then come back for the next one.
+1. You need to copy one value, close the area, paste, then come back for the next one.
 
 
 ```python
@@ -99,7 +100,7 @@ If you already completed the earlier training tutorial, [Train a model](tutorial
 
 If you didn't complete the training tutorial, you need to register the model. Registering your model before deployment is a recommended best practice.
 
-The following code specifies the `path` (where to upload files from) inline. If you [cloned the tutorials folder](quickstart-create-resources.md#learn-from-sample-notebooks), then run the following code as-is. Otherwise, download the files and metadata for the model from the [credit_defaults_model folder](https://github.com/Azure/azureml-examples/tree/main/tutorials/get-started-notebooks/deploy/credit_defaults_model). Save the files you downloaded into a local version of the *credit_defaults_model* folder on your computer and update the path in the following code to the location of the downloaded files.
+The following code specifies the `path` (where to upload files from) inline. If you [cloned the tutorials folder](quickstart-create-resources.md#learn-from-sample-notebooks), run the following code as-is. Otherwise, download the files and metadata for the model from the [credit_defaults_model folder](https://github.com/Azure/azureml-examples/tree/main/tutorials/get-started-notebooks/deploy/credit_defaults_model). Save the files you downloaded into a local version of the *credit_defaults_model* folder on your computer and update the path in the following code to the location of the downloaded files.
 
 The SDK automatically uploads the files and registers the model. 
 
@@ -148,26 +149,25 @@ Now that you have a registered model, you can create an endpoint and deployment.
 
 ## Endpoints and deployments
 
-After you train a machine learning model, you need to deploy it so that others can use it for inferencing. For this purpose, Azure Machine Learning allows you to create **endpoints** and add **deployments** to them.
+After you train a machine learning model, you need to deploy it so others can use it for inferencing. For this purpose, Azure Machine Learning allows you to create **endpoints** and add **deployments** to them.
 
 An **endpoint**, in this context, is an HTTPS path that provides an interface for clients to send requests (input data) to a trained model and receive the inferencing (scoring) results from the model. An endpoint provides:
 
 - Authentication using "key or token" based auth 
 - [TLS(SSL)](https://simple.wikipedia.org/wiki/Transport_Layer_Security) termination
 - A stable scoring URI (endpoint-name.region.inference.ml.azure.com)
 
-
 A **deployment** is a set of resources required for hosting the model that does the actual inferencing. 
 
 A single endpoint can contain multiple deployments. Endpoints and deployments are independent Azure Resource Manager resources that appear in the Azure portal.
 
-Azure Machine Learning allows you to implement [online endpoints](concept-endpoints-online.md) for real-time inferencing on client data, and [batch endpoints](concept-endpoints-batch.md) for inferencing on large volumes of data over a period of time. 
+Azure Machine Learning allows you to implement [online endpoints](concept-endpoints-online.md) for real-time inferencing on client data and [batch endpoints](concept-endpoints-batch.md) for inferencing on large volumes of data over a period of time. 
 
 In this tutorial, you go through the steps of implementing a _managed online endpoint_. Managed online endpoints work with powerful CPU and GPU machines in Azure in a scalable, fully managed way that frees you from the overhead of setting up and managing the underlying deployment infrastructure.
 
 ## Create an online endpoint
 
-Now that you have a registered model, it's time to create your online endpoint. The endpoint name needs to be unique in the entire Azure region. For this tutorial, you create a unique name using a universally unique identifier [`UUID`](https://en.wikipedia.org/wiki/Universally_unique_identifier). For more information on the endpoint naming rules, see [endpoint limits](how-to-manage-quotas.md#azure-machine-learning-online-endpoints-and-batch-endpoints).
+Now that you have a registered model, it's time to create your online endpoint. The endpoint name needs to be unique in the entire Azure region. For this tutorial, you create a unique name using a universally unique identifier [`UUID`](https://en.wikipedia.org/wiki/Universally_unique_identifier). For more information on endpoint naming rules, see [endpoint limits](how-to-manage-quotas.md#azure-machine-learning-online-endpoints-and-batch-endpoints).
 
 
 ```python
@@ -177,12 +177,12 @@ import uuid
 online_endpoint_name = "credit-endpoint-" + str(uuid.uuid4())[:8]
 ```
 
-First, define the endpoint, using the `ManagedOnlineEndpoint` class.
+First, define the endpoint using the `ManagedOnlineEndpoint` class.
 
 
 
 > [!TIP]
-> * `auth_mode` : Use `key` for key-based authentication. Use `aml_token` for Azure Machine Learning token-based authentication. A `key` doesn't expire, but `aml_token` does expire. For more information on authenticating, see [Authenticate clients for online endpoints](how-to-authenticate-online-endpoint.md).
+> * `auth_mode`: Use `key` for key-based authentication. Use `aml_token` for Azure Machine Learning token-based authentication. A `key` doesn't expire, but `aml_token` does expire. For more information on authenticating, see [Authenticate clients for online endpoints](how-to-authenticate-online-endpoint.md).
 >
 > * Optionally, you can add a description and tags to your endpoint.
 
@@ -201,7 +201,7 @@ endpoint = ManagedOnlineEndpoint(
 )
 ```
 
-Using the `MLClient` created earlier, create the endpoint in the workspace. This command starts the endpoint creation and returns a confirmation response while the endpoint creation continues.
+Using the `MLClient` created earlier, create the endpoint in the workspace. This command starts the endpoint creation and returns a confirmation response while endpoint creation continues.
 
 > [!NOTE]
 > Expect the endpoint creation to take approximately 2 minutes.
@@ -233,15 +233,15 @@ The key aspects of a deployment include:
 - `endpoint_name` - Name of the endpoint that will contain the deployment.
 - `model` - The model to use for the deployment. This value can be either a reference to an existing versioned model in the workspace or an inline model specification.
 - `environment` - The environment to use for the deployment (or to run the model). This value can be either a reference to an existing versioned environment in the workspace or an inline environment specification. The environment can be a Docker image with Conda dependencies or a Dockerfile.
-- `code_configuration` - the configuration for the source code and scoring script.
-    - `path`- Path to the source code directory for scoring the model.
+- `code_configuration` - The configuration for the source code and scoring script.
+    - `path` - Path to the source code directory for scoring the model.
     - `scoring_script` - Relative path to the scoring file in the source code directory. This script executes the model on a given input request. For an example of a scoring script, see [Understand the scoring script](how-to-deploy-online-endpoints.md#understand-the-scoring-script) in the "Deploy an ML model with an online endpoint" article.
 - `instance_type` - The VM size to use for the deployment. For the list of supported sizes, see [Managed online endpoints SKU list](reference-managed-online-endpoints-vm-sku-list.md).
 - `instance_count` - The number of instances to use for the deployment.
 
 ### Deployment using an MLflow model
 
-Azure Machine Learning supports no-code deployment of a model created and logged with MLflow. This means that you don't have to provide a scoring script or an environment during model deployment, as the scoring script and environment are automatically generated when training an MLflow model. If you were using a custom model, though, you'd have to specify the environment and scoring script during deployment.
+Azure Machine Learning supports no-code deployment of a model created and logged with MLflow. This means you don't have to provide a scoring script or an environment during model deployment, as the scoring script and environment are automatically generated when training an MLflow model. If you were using a custom model, though, you'd have to specify the environment and scoring script during deployment.
 
 > [!IMPORTANT]
 > If you typically deploy models using scoring scripts and custom environments and want to achieve the same functionality using MLflow models, we recommend reading [Guidelines for deploying MLflow models](how-to-deploy-mlflow-models.md).
@@ -251,7 +251,7 @@ Azure Machine Learning supports no-code deployment of a model created and logged
 Begin by creating a single deployment that handles 100% of the incoming traffic. Choose an arbitrary color name (*blue*) for the deployment. To create the deployment for the endpoint, use the `ManagedOnlineDeployment` class.
 
 > [!NOTE]
-> No need to specify an environment or scoring script as the model to deploy is an MLflow model.
+> You don't need to specify an environment or scoring script as the model to deploy is an MLflow model.
 
 
 ```python
@@ -261,7 +261,7 @@ from azure.ai.ml.entities import ManagedOnlineDeployment
 model = ml_client.models.get(name=registered_model_name, version=latest_model_version)
 
 # define an online deployment
-# if you run into an out of quota error, change the instance_type to a comparable VM that is available.\
+# if you run into an out of quota error, change the instance_type to a comparable VM that is available.
 # Learn more on https://azure.microsoft.com/en-us/pricing/details/machine-learning/.
 blue_deployment = ManagedOnlineDeployment(
     name="blue",
@@ -272,7 +272,7 @@ blue_deployment = ManagedOnlineDeployment(
 )
 ```
 
-Using the `MLClient` created earlier, now create the deployment in the workspace. This command starts the deployment creation and returns a confirmation response while the deployment creation continues.
+Using the `MLClient` created earlier, create the deployment in the workspace. This command starts the deployment creation and returns a confirmation response while deployment creation continues.
 
 
 ```python
@@ -288,6 +288,7 @@ ml_client.online_endpoints.begin_create_or_update(endpoint).result()
 ```
 
 ## Check the status of the endpoint
+
 You can check the status of the endpoint to see whether the model was deployed without error:
 
 
@@ -312,7 +313,7 @@ print(endpoint.scoring_uri)
 
 ## Test the endpoint with sample data
 
-Now that the model is deployed to the endpoint, you can run inference with it. Begin by creating a sample request file that follows the design expected in the run method found in the scoring script.
+Now that the model is deployed to the endpoint, you can run inference with it. Start by creating a sample request file that follows the design expected in the run method found in the scoring script.
 
 
 ```python
@@ -323,7 +324,7 @@ deploy_dir = "./deploy"
 os.makedirs(deploy_dir, exist_ok=True)
 ```
 
-Now, create the file in the deploy directory. The following code cell uses IPython magic to write the file into the directory you just created.
+Now create the file in the deploy directory. The following code cell uses IPython magic to write the file into the directory you created.
 
 
 ```python
@@ -359,7 +360,8 @@ ml_client.online_endpoints.invoke(
 ```
 
 ## Get logs of the deployment
-Check the logs to see whether the endpoint/deployment were invoked successfully.
+
+Check the logs to see whether the endpoint/deployment was invoked successfully.
 If you face errors, see [Troubleshooting online endpoints deployment](how-to-troubleshoot-online-endpoints.md).
 
 
@@ -381,7 +383,7 @@ In this example, you deploy the same model version, using a more powerful comput
 model = ml_client.models.get(name=registered_model_name, version=latest_model_version)
 
 # define an online deployment using a more powerful instance type
-# if you run into an out of quota error, change the instance_type to a comparable VM that is available.\
+# if you run into an out of quota error, change the instance_type to a comparable VM that is available.
 # Learn more on https://azure.microsoft.com/en-us/pricing/details/machine-learning/.
 green_deployment = ManagedOnlineDeployment(
     name="green",
@@ -415,6 +417,7 @@ ml_client.online_deployments.begin_create_or_update(green_deployment).result()
 ```
 
 ## Update traffic allocation for deployments
+
 You can split production traffic between deployments. You might first want to test the `green` deployment with sample data, just like you did for the `blue` deployment. Once you've tested your green deployment, allocate a small percentage of traffic to it.
 
 
@@ -457,6 +460,7 @@ If you open the metrics for the online endpoint, you can set up the page to see
 For more information on how to view online endpoint metrics, see [Monitor online endpoints](how-to-monitor-online-endpoints.md#use-metrics).
 
 ## Send all traffic to the new deployment
+
 Once you're fully satisfied with your `green` deployment, switch all traffic to it.
 
 
@@ -466,6 +470,7 @@ ml_client.begin_create_or_update(endpoint).result()
 ```
 
 ## Delete the old deployment
+
 Remove the old (blue) deployment:
 
 
@@ -477,7 +482,7 @@ ml_client.online_deployments.begin_delete(
 
 ## Clean up resources
 
-If you aren't going use the endpoint and deployment after completing this tutorial, you should delete them.
+If you aren't going to use the endpoint and deployment after completing this tutorial, you should delete them.
 
 > [!NOTE]
 > Expect the complete deletion to take approximately 20 minutes.