Merge pull request #220230 from msakande/rename-file-for-online-deployment

Rename file for online deployment

Author: v-shils

articles/machine-learning/.openpublishing.redirection.machine-learning.json

@@ -1567,17 +1567,17 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-deploy-local-container-notebook-vm.md",
-        "redirect_url": "/azure/machine-learning/how-to-deploy-managed-online-endpoints",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-deploy-local.md",
-        "redirect_url": "/azure/machine-learning/how-to-deploy-managed-online-endpoints",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-deploy-managed-online-endpoint-sdk-v2.md",
-        "redirect_url": "/azure/machine-learning/how-to-deploy-managed-online-endpoints",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {
@@ -1587,12 +1587,12 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-deploy-update-web-service.md",
-        "redirect_url": "/azure/machine-learning/how-to-deploy-managed-online-endpoints",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-deploy-inferencing-gpus.md",
-        "redirect_url": "/azure/machine-learning/how-to-deploy-managed-online-endpoints",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {
@@ -2472,7 +2472,7 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-deploy-functions.md",
-        "redirect_url": "/azure/machine-learning/how-to-deploy-managed-online-endpoints",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {
@@ -2482,7 +2482,7 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-deploy-and-where.md",
-        "redirect_url": "/articles/machine-learning/how-to-deploy-managed-online-endpoints",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {
@@ -2507,7 +2507,12 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-deploy-app-service.md",
-        "redirect_url": "/azure/machine-learning/how-to-deploy-managed-online-endpoints",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
+        "redirect_document_id": false
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-deploy-managed-online-endpoints.md",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {

articles/machine-learning/concept-compute-target.md

@@ -39,7 +39,7 @@ When performing inference, Azure Machine Learning creates a Docker container tha
 
 [!INCLUDE [aml-deploy-target](../../includes/aml-compute-target-deploy.md)]
 
-Learn [where and how to deploy your model to a compute target](how-to-deploy-managed-online-endpoints.md).
+Learn [where and how to deploy your model to a compute target](how-to-deploy-online-endpoints.md).
 
 ## Azure Machine Learning compute (managed)
 
@@ -170,4 +170,4 @@ For more information, see [Manage compute resources](how-to-create-attach-comput
 ## Next steps
 
 Learn how to:
-* [Deploy your model to a compute target](how-to-deploy-managed-online-endpoints.md)
+* [Deploy your model to a compute target](how-to-deploy-online-endpoints.md)

articles/machine-learning/concept-endpoints.md

@@ -70,7 +70,7 @@ To create an online endpoint, you need to specify the following elements:
 - Environment - a Docker image with Conda dependencies, or a dockerfile 
 - Compute instance & scale settings 
 
-Learn how to deploy online endpoints from the [CLI](how-to-deploy-managed-online-endpoints.md) and the [studio web portal](how-to-use-managed-online-endpoint-studio.md).
+Learn how to deploy online endpoints from the [CLI](how-to-deploy-online-endpoints.md) and the [studio web portal](how-to-use-managed-online-endpoint-studio.md).
 
 ### Test and deploy locally for faster debugging
 
@@ -145,7 +145,7 @@ The following table highlights the key differences between managed online endpoi
 | **Infrastructure management** | Managed compute provisioning, scaling, host OS image updates, and security hardening | User responsibility |
 | **Compute type** | Managed (AmlCompute) | Kubernetes cluster (Kubernetes) |
 | **Out-of-box monitoring** | [Azure Monitoring](how-to-monitor-online-endpoints.md) <br> (includes key metrics like latency and throughput) | Supported |
-| **Out-of-box logging** | [Azure Logs and Log Analytics at endpoint level](how-to-deploy-managed-online-endpoints.md#optional-integrate-with-log-analytics) | 	Unsupported |
+| **Out-of-box logging** | [Azure Logs and Log Analytics at endpoint level](how-to-deploy-online-endpoints.md#optional-integrate-with-log-analytics) | 	Unsupported |
 | **Application Insights** | Supported | Supported |
 | **Managed identity** | [Supported](how-to-access-resources-from-endpoints-managed-identities.md) | Supported |
 | **Virtual Network (VNET)** | [Supported](how-to-secure-online-endpoint.md) (preview) | Supported |
@@ -178,7 +178,7 @@ Managed online endpoints can help streamline your deployment process. Managed on
     >
     > If you use a virtual network and secure outbound (egress) traffic from the managed online endpoint, there is an additional cost. For egress, three private endpoints are created _per deployment_ for the managed online endpoint. These are used to communicate with the default storage account, Azure Container Registry, and workspace. Additional networking charges may apply. For more information on pricing, see the [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator/).
 
-For a step-by-step tutorial, see [How to deploy online endpoints](how-to-deploy-managed-online-endpoints.md).
+For a step-by-step tutorial, see [How to deploy online endpoints](how-to-deploy-online-endpoints.md).
 
 ## What are batch endpoints?
 
@@ -235,7 +235,7 @@ Specify the storage output location to any datastore and path. By default, batch
 
 ## Next steps
 
-- [How to deploy online endpoints with the Azure CLI and Python SDK](how-to-deploy-managed-online-endpoints.md)
+- [How to deploy online endpoints with the Azure CLI and Python SDK](how-to-deploy-online-endpoints.md)
 - [How to deploy batch endpoints with the Azure CLI and Python SDK](batch-inference/how-to-use-batch-endpoint.md)
 - [How to use online endpoints with the studio](how-to-use-managed-online-endpoint-studio.md)
 - [Deploy models with REST](how-to-deploy-with-rest.md)

articles/machine-learning/concept-model-management-and-deployment.md

@@ -126,7 +126,7 @@ To deploy the model to an endpoint, you must provide the following items:
 * Dependencies required to use the model. Examples are a script that accepts requests and invokes the model and conda dependencies.
 * Deployment configuration that describes how and where to deploy the model.
 
-For more information, see [Deploy online endpoints](how-to-deploy-managed-online-endpoints.md).
+For more information, see [Deploy online endpoints](how-to-deploy-online-endpoints.md).
 
 #### Controlled rollout
 
@@ -179,7 +179,7 @@ For more information on using Azure Pipelines with Machine Learning, see:
 Learn more by reading and exploring the following resources:
 
 + [Learning path: End-to-end MLOps with Azure Machine Learning](/training/paths/build-first-machine-operations-workflow/)
-+ [How to deploy a model to an online endpoint](how-to-deploy-managed-online-endpoints.md) with Machine Learning
++ [How to deploy a model to an online endpoint](how-to-deploy-online-endpoints.md) with Machine Learning
 + [Tutorial: Train and deploy a model](tutorial-train-deploy-notebook.md)
 + [CI/CD of machine learning models with Azure Pipelines](/azure/devops/pipelines/targets/azure-machine-learning)
 + [Machine learning at scale](/azure/architecture/data-guide/big-data/machine-learning-at-scale)

articles/machine-learning/concept-prebuilt-docker-images-inference.md

@@ -37,6 +37,6 @@ Prebuilt Docker container images for inference are used when deploying a model w
 
 ## Next steps
 
-* [Deploy and score a machine learning model by using an online endpoint](how-to-deploy-managed-online-endpoints.md)
+* [Deploy and score a machine learning model by using an online endpoint](how-to-deploy-online-endpoints.md)
 * [Learn more about custom containers](how-to-deploy-custom-container.md)
 * [azureml-examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online)

articles/machine-learning/concept-v2.md

@@ -117,7 +117,7 @@ The Azure Machine Learning Python SDK v1 doesn't have a planned deprecation date
 
     * [Install and set up CLI (v2)](how-to-configure-cli.md)
     * [Train models with the CLI (v2)](how-to-train-model.md)
-    * [Deploy and score models with managed online endpoint](how-to-deploy-managed-online-endpoints.md)
+    * [Deploy and score models with online endpoints](how-to-deploy-online-endpoints.md)
 
 * Get started with SDK v2
 

articles/machine-learning/how-to-access-resources-from-endpoints-managed-identities.md

@@ -898,7 +898,7 @@ 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 online endpoint. [Learn more about deploying to online endpoints](how-to-deploy-managed-online-endpoints.md).
+Create a deployment that's associated with the online endpoint. [Learn more about deploying to online endpoints](how-to-deploy-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.
@@ -1116,7 +1116,7 @@ msi_client.user_assigned_identities.delete(
 
 ## Next steps
 
-* [Deploy and score a machine learning model by using a online endpoint](how-to-deploy-managed-online-endpoints.md).
+* [Deploy and score a machine learning model by using an online endpoint](how-to-deploy-online-endpoints.md).
 * For more on deployment, see [Safe rollout for online endpoints](how-to-safely-rollout-online-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](reference-managed-online-endpoints-vm-sku-list.md).

articles/machine-learning/how-to-authenticate-online-endpoint.md

@@ -21,7 +21,7 @@ When consuming an online endpoint from a client, you can use either a _key_ or a
 
 You can set the authentication type when you create an online endpoint. Set the `auth_mode` to `key` or `aml_token` depending on which one you want to use. The default value is `key`.
 
-When deploying using CLI v2, set this value in the [online endpoint YAML file](reference-yaml-endpoint-online.md). For more information, see [How to deploy an online endpoint](how-to-deploy-managed-online-endpoints.md).
+When deploying using CLI v2, set this value in the [online endpoint YAML file](reference-yaml-endpoint-online.md). For more information, see [How to deploy an online endpoint](how-to-deploy-online-endpoints.md).
 
 When deploying using the Python SDK v2, use the [OnlineEndpoint](/python/api/azure-ai-ml/azure.ai.ml.entities.onlineendpoint) class.
 
@@ -47,5 +47,5 @@ When calling the online endpoint for scoring, pass the key or token in the autho
 
 ## Next steps
 
-* [Deploy a machine learning model using an online endpoint](how-to-deploy-managed-online-endpoints.md)
+* [Deploy a machine learning model using an online endpoint](how-to-deploy-online-endpoints.md)
 * [Enable network isolation for managed online endpoints](how-to-secure-online-endpoint.md)

articles/machine-learning/how-to-autoscale-endpoints.md

@@ -24,7 +24,7 @@ Today, you can manage autoscaling using either the Azure CLI, REST, ARM, or the
 
 ## Prerequisites
 
-* A deployed endpoint. [Deploy and score a machine learning model by using an online endpoint](how-to-deploy-managed-online-endpoints.md). 
+* A deployed endpoint. [Deploy and score a machine learning model by using an online endpoint](how-to-deploy-online-endpoints.md). 
 
 ## Define an autoscale profile
 

articles/machine-learning/how-to-configure-auto-train.md

@@ -440,4 +440,4 @@ For more examples on how to do include AutoML in your pipelines, please check ou
 
 ## Next steps
 
-+ Learn more about [how and where to deploy a model](./how-to-deploy-managed-online-endpoints.md).
++ Learn more about [how and where to deploy a model](./how-to-deploy-online-endpoints.md).