Update naming

luisquintanilla · luisquintanilla · commit c061d2d021b6 · 2021-12-22T16:10:46.000-05:00
diff --git a/articles/machine-learning/concept-endpoints.md b/articles/machine-learning/concept-endpoints.md
@@ -10,7 +10,7 @@ ms.author: seramasu
 author: rsethur
 ms.reviewer: laobri
 ms.custom: devplatv2, ignite-fall-2021
-ms.date: 10/21/2021
+ms.date: 12/22/2021
 #Customer intent: As an MLOps administrator, I want to understand what a managed endpoint is and why I need it.
 ---
 
@@ -97,7 +97,7 @@ However [managed online endpoints](#managed-online-endpoints-vs-kubernetes-onlin
 ### Security
 
 - Authentication: Key and Azure ML Tokens
-- Managed identity: User assigned and system assigned (managed online endpoint only)
+- Managed identity: User assigned and system assigned
 - SSL by default for endpoint invocation
 
 ### Autoscaling
@@ -145,15 +145,12 @@ Managed online endpoints can help streamline your deployment process. Managed on
 
     :::image type="content" source="media/concept-endpoints/log-analytics-and-azure-monitor.png" alt-text="Screenshot showing Azure Monitor graph of endpoint latency":::
 
-- Managed identity
-    -  Use [managed identities to access secured resources from scoring script](tutorial-deploy-managed-endpoints-using-system-managed-identity.md)
-
 - View costs 
     - Managed online endpoints let you [monitor cost at the endpoint and deployment level](how-to-view-online-endpoints-costs.md)
     
     :::image type="content" source="media/concept-endpoints/endpoint-deployment-costs.png" alt-text="Screenshot cost chart of an endpoint and deployment":::
 
-For a step-by-step tutorial, see [How to deploy managed online endpoints](how-to-deploy-managed-online-endpoints.md).
+For a step-by-step tutorial, see [How to deploy online endpoints](how-to-deploy-managed-online-endpoints.md).
 
 ## What are batch endpoints (preview)?
 
@@ -197,10 +194,10 @@ Specify the storage output location to any datastore and path. By default, batch
 
 ## Next steps
 
-- [How to deploy managed online endpoints with the Azure CLI](how-to-deploy-managed-online-endpoints.md)
+- [How to deploy online endpoints with the Azure CLI](how-to-deploy-managed-online-endpoints.md)
 - [How to deploy batch endpoints with the Azure CLI](how-to-use-batch-endpoint.md)
-- [How to use managed online endpoints with the studio](how-to-use-managed-online-endpoint-studio.md)
+- [How to use online endpoints with the studio](how-to-use-managed-online-endpoint-studio.md)
 - [Deploy models with REST (preview)](how-to-deploy-with-rest.md)
 - [How to monitor managed online endpoints](how-to-monitor-online-endpoints.md)
-- [How to view online endpoint costs](how-to-view-online-endpoints-costs.md)
+- [How to view managed online endpoint costs](how-to-view-online-endpoints-costs.md)
 - [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints-preview)
diff --git a/articles/machine-learning/how-to-access-resources-from-endpoints-managed-identities.md b/articles/machine-learning/how-to-access-resources-from-endpoints-managed-identities.md
@@ -1,27 +1,27 @@
 ---
 title: Access Azure resources from managed endpoint
 titleSuffix: Azure Machine Learning
-description: Securely access Azure resources for your machine learning model deployment from a managed online endpoint with a system-assigned or user-assigned managed identity.
+description: Securely access Azure resources for your machine learning model deployment from an online endpoint with a system-assigned or user-assigned managed identity.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.author: seramasu
 ms.reviewer: laobri
 author: rsethur
-ms.date: 10/21/2021
+ms.date: 12/22/2021
 ms.topic: how-to
 ms.custom: devplatv2
 
-# Customer intent: As a data scientist, I want to securely access Azure resources for my machine learning model deployment with a managed online endpoint and managed identity. 
+# Customer intent: As a data scientist, I want to securely access Azure resources for my machine learning model deployment with an online endpoint and managed identity. 
 ---
 
-# Access Azure resources from a managed online endpoint (preview) with a managed identity 
+# Access Azure resources from an online endpoint (preview) with a managed identity 
 
-Learn how to access Azure resources from your scoring script with a managed online endpoint and either a system-assigned managed identity or a user-assigned managed identity. 
+Learn how to access Azure resources from your scoring script with an online endpoint and either a system-assigned managed identity or a user-assigned managed identity. 
 
 Managed endpoints (preview) allow Azure Machine Learning to manage the burden of provisioning your compute resource and deploying your machine learning model. Typically your model needs to access Azure resources such as the Azure Container Registry or your blob storage for inferencing; with a managed identity you can access these resources without needing to manage credentials in your code. [Learn more about managed identities](../active-directory/managed-identities-azure-resources/overview.md).
 
-This guide assumes you don't have a managed identity, a storage account or a managed online endpoint. If you already have these components, skip to the [give access permission to the managed identity](#give-access-permission-to-the-managed-identity) section. 
+This guide assumes you don't have a managed identity, a storage account or an online endpoint. If you already have these components, skip to the [give access permission to the managed identity](#give-access-permission-to-the-managed-identity) section. 
 
 [!INCLUDE [preview disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
 
@@ -184,9 +184,9 @@ Then, upload file in container.
 
 ---
 
-## Create a managed online endpoint
+## Create an online endpoint
 
-The following code creates a managed online endpoint without specifying a deployment. 
+The following code creates an online endpoint without specifying a deployment. 
 
 # [System-assigned managed identity](#tab/system-identity)
 When you create a managed endpoint, a system-assigned managed identity is created for the endpoint by default.
@@ -200,7 +200,7 @@ Check the status of the endpoint with the following.
 
 ::: code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint-access-resource-sai.sh" id="check_endpoint_Status" :::
 
-If you encounter any issues, see [Troubleshooting managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md).
+If you encounter any issues, see [Troubleshooting online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md).
 
 # [User-assigned managed identity](#tab/user-identity)
 
@@ -210,7 +210,7 @@ Check the status of the endpoint with the following.
 
 ::: code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint-access-resource-uai.sh" id="check_endpoint_Status" :::
 
-If you encounter any issues, see [Troubleshooting managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md).
+If you encounter any issues, see [Troubleshooting online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md).
 
 ---
 
@@ -271,7 +271,7 @@ Refer to the following script to understand how to use your identity token to ac
 
 ## Create a deployment with your configuration
 
-Create a deployment that's associated with the managed endpoint. [Learn more about deploying to managed online endpoints](how-to-deploy-managed-online-endpoints.md).
+Create a deployment that's associated with the managed endpoint. [Learn more about deploying to online endpoints](how-to-deploy-managed-online-endpoints.md).
 
 >[!WARNING]
 > This deployment can take approximately 8-14 minutes depending on whether the underlying environment/image is being built for the first time. Subsequent deployments using the same environment will go quicker.
@@ -360,7 +360,7 @@ If you don't plan to continue using the deployed endpoint and storage, delete th
 
 ## Next steps
 
-* [Deploy and score a machine learning model by using a managed online endpoint (preview)](how-to-deploy-managed-online-endpoints.md).
+* [Deploy and score a machine learning model by using a online endpoint (preview)](how-to-deploy-managed-online-endpoints.md).
 * For more on deployment, see [Safe rollout for online endpoints (preview)](how-to-safely-rollout-managed-endpoints.md).
 * For more information on using the CLI, see [Use the CLI extension for Azure Machine Learning](reference-azure-machine-learning-cli.md).
 * To see which compute resources you can use, see [Managed online endpoints SKU list (preview)](reference-managed-online-endpoints-vm-sku-list.md).
diff --git a/articles/machine-learning/how-to-deploy-automl-endpoint.md b/articles/machine-learning/how-to-deploy-automl-endpoint.md
@@ -1,5 +1,5 @@
 ---
-title: Deploy an AutoML model with a managed online endpoint (preview)
+title: Deploy an AutoML model with an online endpoint (preview)
 titleSuffix: Azure Machine Learning
 description: Learn to deploy your AutoML model as a web service that's automatically managed by Azure.
 services: machine-learning
@@ -8,7 +8,7 @@ ms.subservice: core
 ms.author: ssambare
 ms.reviewer: laobri
 author: shivanissambare
-ms.date: 10/21/2021
+ms.date: 12/22/2021
 ms.topic: how-to
 ms.custom: how-to, devplatv2
 ---
@@ -115,7 +115,7 @@ Create a directory called `src/` and place the scoring file you downloaded into
 
 ## Create the endpoint and deployment yaml file
 
-To create a managed online endpoint from the command line, you'll need to create an *endpoint.yml* and a *deployment.yml* file. The following code, taken from the [Azure Machine Learning Examples repo](https://github.com/Azure/azureml-examples) shows the _endpoints/online/managed/sample/_, which captures all the required inputs:
+To create an online endpoint from the command line, you'll need to create an *endpoint.yml* and a *deployment.yml* file. The following code, taken from the [Azure Machine Learning Examples repo](https://github.com/Azure/azureml-examples) shows the _endpoints/online/managed/sample/_, which captures all the required inputs:
 
 __automl_endpoint.yml__
 
@@ -156,5 +156,5 @@ After you create a deployment, you can score it as described in [Invoke the endp
 
 ## Next steps
 
-- [Troubleshooting managed online endpoints deployment](how-to-troubleshoot-managed-online-endpoints.md)
+- [Troubleshooting online endpoints deployment](how-to-troubleshoot-managed-online-endpoints.md)
 - [Safe rollout for online endpoints](how-to-safely-rollout-managed-endpoints.md)
diff --git a/articles/machine-learning/how-to-deploy-custom-container.md b/articles/machine-learning/how-to-deploy-custom-container.md
@@ -1,5 +1,5 @@
 ---
-title: Deploy a custom container as a managed online endpoint
+title: Deploy a custom container as an online endpoint
 titleSuffix: Azure Machine Learning
 description: Learn how to use a custom container to use open-source servers in Azure Machine Learning.
 services: machine-learning
@@ -8,14 +8,14 @@ ms.subservice: mlops
 ms.author: ssambare
 author: shivanissambare
 ms.reviewer: larryfr
-ms.date: 10/21/2021
+ms.date: 12/22/2021
 ms.topic: how-to
 ms.custom: deploy, devplatv2
 ---
 
-# Deploy a TensorFlow model served with TF Serving using a custom container in a managed online endpoint (preview)
+# Deploy a TensorFlow model served with TF Serving using a custom container in an online endpoint (preview)
 
-Learn how to deploy a custom container as a managed online endpoint in Azure Machine Learning.
+Learn how to deploy a custom container as an online endpoint in Azure Machine Learning.
 
 Custom container deployments can use web servers other than the default Python Flask server used by Azure Machine Learning. Users of these deployments can still take advantage of Azure Machine Learning's built-in monitoring, scaling, alerting, and authentication.
 
@@ -183,5 +183,5 @@ az ml model delete -n tfserving-mounted --version 1
 ## Next steps
 
 - [Safe rollout for online endpoints (preview)](how-to-safely-rollout-managed-endpoints.md)
-- [Troubleshooting managed online endpoints deployment](./how-to-troubleshoot-online-endpoints.md)
+- [Troubleshooting online endpoints deployment](./how-to-troubleshoot-online-endpoints.md)
 - [Torch serve sample](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-torchserve.sh)
diff --git a/articles/machine-learning/how-to-deploy-managed-online-endpoints.md b/articles/machine-learning/how-to-deploy-managed-online-endpoints.md
@@ -8,7 +8,7 @@ ms.subservice: mlops
 ms.author: seramasu
 ms.reviewer: laobri
 author: rsethur
-ms.date: 10/21/2021
+ms.date: 12/22/2021
 ms.topic: how-to
 ms.custom: how-to, devplatv2, ignite-fall-2021
 ---
@@ -139,7 +139,7 @@ As noted earlier, the `code_configuration.scoring_script` must have an `init()`
 
 ## Deploy and debug locally by using local endpoints
 
-To save time debugging, we *highly recommend* that you test-run your endpoint locally. For more, see [Debug managed online endpoints locally in Visual Studio Code](how-to-debug-managed-online-endpoints-visual-studio-code.md).
+To save time debugging, we *highly recommend* that you test-run your endpoint locally. For more, see [Debug online endpoints locally in Visual Studio Code](how-to-debug-managed-online-endpoints-visual-studio-code.md).
 
 > [!NOTE]
 > * To deploy locally, [Docker Engine](https://docs.docker.com/engine/install/) must be installed.
@@ -163,7 +163,7 @@ Now, create a deployment named `blue` under the endpoint.
 The `--local` flag directs the CLI to deploy the endpoint in the Docker environment.
 
 > [!TIP]
-> Use Visual Studio Code to test and debug your endpoints locally. For more information, see [debug managed online endpoints locally in Visual Studio Code](how-to-debug-managed-online-endpoints-visual-studio-code.md).
+> Use Visual Studio Code to test and debug your endpoints locally. For more information, see [debug online endpoints locally in Visual Studio Code](how-to-debug-managed-online-endpoints-visual-studio-code.md).
 
 ### Verify the local deployment succeeded
 
@@ -200,9 +200,9 @@ In the example *score.py* file, the `run()` method logs some output to the conso
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/deploy-local-endpoint.sh" ID="get_logs":::
 
-##  Deploy your managed online endpoint to Azure
+##  Deploy your online endpoint to Azure
 
-Next, deploy your managed online endpoint to Azure.
+Next, deploy your online endpoint to Azure.
 
 ### Deploy to Azure
 
@@ -223,7 +223,7 @@ This deployment might take up to 15 minutes, depending on whether the underlying
 > [!TIP]
 > * If you prefer not to block your CLI console, you may add the flag `--no-wait` to the command. However, this will stop the interactive display of the deployment status.
 >
-> * Use [Troubleshooting managed online endpoints deployment (preview)](./how-to-troubleshoot-online-endpoints.md) to debug errors.
+> * Use [Troubleshooting online endpoints deployment (preview)](./how-to-troubleshoot-online-endpoints.md) to debug errors.
 
 ### Check the status of the deployment
 
@@ -336,10 +336,10 @@ If you aren't going use the deployment, you should delete it by running the foll
 To learn more, review these articles:
 
 - [Deploy models with REST (preview)](how-to-deploy-with-rest.md)
-- [Create and use managed online endpoints (preview) in the studio](how-to-use-managed-online-endpoint-studio.md)
+- [Create and use online endpoints (preview) in the studio](how-to-use-managed-online-endpoint-studio.md)
 - [Safe rollout for online endpoints (preview)](how-to-safely-rollout-managed-endpoints.md)
 - [How to autoscale managed online endpoints](how-to-autoscale-endpoints.md)
 - [Use batch endpoints (preview) for batch scoring](how-to-use-batch-endpoint.md)
 - [View costs for an Azure Machine Learning managed online endpoint (preview)](how-to-view-online-endpoints-costs.md)
-- [Access Azure resources with a managed online endpoint and managed identity (preview)](how-to-access-resources-from-endpoints-managed-identities.md)
-- [Troubleshoot managed online endpoints deployment](how-to-troubleshoot-online-endpoints.md)
+- [Access Azure resources with a online endpoint and managed identity (preview)](how-to-access-resources-from-endpoints-managed-identities.md)
+- [Troubleshoot online endpoints deployment](how-to-troubleshoot-online-endpoints.md)
diff --git a/articles/machine-learning/how-to-deploy-with-rest.md b/articles/machine-learning/how-to-deploy-with-rest.md
@@ -1,15 +1,15 @@
 ---
-title: "Deploy models using managed online endpoints with REST APIs (preview)"
+title: "Deploy models using online endpoints with REST APIs (preview)"
 titleSuffix: Azure Machine Learning
-description: Learn how to deploy models using managed online endpoints with REST APIs.
+description: Learn how to deploy models using online endpoints with REST APIs.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: how-to
 
 author: rsethur
 ms.author: seramasu
-ms.date: 10/21/2021
+ms.date: 12/22/2021
 ms.reviewer: laobri
 ms.custom: devplatv2
 ---
@@ -44,10 +44,11 @@ In this article, you learn how to use the new REST APIs to:
 
 :::code language="rest-api" source="~/azureml-examples-main/cli/deploy-rest.sh" id="set_endpoint_name":::
 
-## Azure Machine Learning managed online endpoints
-Managed online endpoints (preview) allow you to deploy your model without having to create and manage the underlying infrastructure. In this article, you'll create an online endpoint and deployment, and validate it by invoking it. But first you'll have to register the assets needed for deployment, including model, code, and environment.
+## Azure Machine Learning online endpoints
 
-There are many ways to create an Azure Machine Learning online endpoints [including the Azure CLI](how-to-deploy-managed-online-endpoints.md), and visually with [the studio](how-to-use-managed-online-endpoint-studio.md). The following example a managed online endpoint with the REST API.
+Online endpoints (preview) allow you to deploy your model without having to create and manage the underlying infrastructure as well as Kubernetes clusters. In this article, you'll create an online endpoint and deployment, and validate it by invoking it. But first you'll have to register the assets needed for deployment, including model, code, and environment.
+
+There are many ways to create an Azure Machine Learning online endpoints [including the Azure CLI](how-to-deploy-managed-online-endpoints.md), and visually with [the studio](how-to-use-managed-online-endpoint-studio.md). The following example an online endpoint with the REST API.
 
 ## Create machine learning assets
 
@@ -143,8 +144,8 @@ If you aren't going use the deployment, you should delete it with the below comm
 
 * Learn how to deploy your model [using the Azure CLI](how-to-deploy-managed-online-endpoints.md).
 * Learn how to deploy your model [using studio](how-to-use-managed-online-endpoint-studio.md).
-* Learn to [Troubleshoot managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md)
-* Learn how to [Access Azure resources with a managed online endpoint and managed identity (preview)](how-to-access-resources-from-endpoints-managed-identities.md)
+* Learn to [Troubleshoot online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md)
+* Learn how to [Access Azure resources with a online endpoint and managed identity (preview)](how-to-access-resources-from-endpoints-managed-identities.md)
 * Learn how to [monitor online endpoints](how-to-monitor-online-endpoints.md).
 * Learn [Safe rollout for online endpoints (preview)](how-to-safely-rollout-managed-endpoints.md).
 * [View costs for an Azure Machine Learning managed online endpoint (preview)](how-to-view-online-endpoints-costs.md).
diff --git a/articles/machine-learning/how-to-safely-rollout-managed-endpoints.md b/articles/machine-learning/how-to-safely-rollout-managed-endpoints.md
diff --git a/articles/machine-learning/toc.yml b/articles/machine-learning/toc.yml
