Merge pull request #253173 from msakande/remove-preview-for-workspace-vnet

removing preview and include file

Author: prmerger-automator[bot]

articles/machine-learning/concept-endpoints-online.md

@@ -10,7 +10,7 @@ author: dem108
 ms.author: sehan
 ms.reviewer: mopeakande
 reviewer: msakande
-ms.custom: devplatv2, moe-wsvnet
+ms.custom: devplatv2
 ms.date: 09/13/2023
 
 #Customer intent: As an MLOps administrator, I want to understand what a managed endpoint is and why I need it.

articles/machine-learning/concept-secure-network-traffic-flow.md

@@ -5,7 +5,7 @@ description: Learn how network traffic flows between components when your Azure
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: enterprise-readiness
-ms.custom: event-tier1-build-2022, moe-wsvnet
+ms.custom: event-tier1-build-2022
 ms.topic: conceptual
 ms.author: jhirono
 author: jhirono
@@ -144,10 +144,9 @@ The `public_network_access` flag of the Azure Machine Learning workspace also go
 
 #### Outbound communication
 
-__Outbound__ communication from a deployment can be secured at the workspace level by enabling managed virtual network isolation for your Azure Machine Learning workspace (preview). Enabling this setting causes Azure Machine Learning to create a managed virtual network for the workspace. Any deployments in the workspace's managed virtual network can use the virtual network's private endpoints for outbound communication.
-[!INCLUDE [machine-learning-preview-generic-disclaimer](includes/machine-learning-preview-generic-disclaimer.md)]
+__Outbound__ communication from a deployment can be secured at the workspace level by enabling managed virtual network isolation for your Azure Machine Learning workspace. Enabling this setting causes Azure Machine Learning to create a managed virtual network for the workspace. Any deployments in the workspace's managed virtual network can use the virtual network's private endpoints for outbound communication.
 
-The [legacy network isolation method for securing outbound communication](concept-secure-online-endpoint.md#secure-outbound-access-with-legacy-network-isolation-method) worked by disabling a deployment's `egress_public_network_access` flag. We strongly recommend that you secure outbound communication for deployments by using a [workspace managed virtual network](concept-secure-online-endpoint.md) instead. Unlike the legacy approach, the `egress_public_network_access` flag for the deployment no longer applies when you use a workspace managed virtual network with your deployment (preview). Instead, outbound communication will be controlled by the rules set for the workspace's managed virtual network.
+The [legacy network isolation method for securing outbound communication](concept-secure-online-endpoint.md#secure-outbound-access-with-legacy-network-isolation-method) worked by disabling a deployment's `egress_public_network_access` flag. We strongly recommend that you secure outbound communication for deployments by using a [workspace managed virtual network](concept-secure-online-endpoint.md) instead. Unlike the legacy approach, the `egress_public_network_access` flag for the deployment no longer applies when you use a workspace managed virtual network with your deployment. Instead, outbound communication will be controlled by the rules set for the workspace's managed virtual network.
 
 :::moniker-end
 

articles/machine-learning/concept-secure-online-endpoint.md

@@ -10,7 +10,7 @@ author: dem108
 ms.author: sehan
 ms.reviewer: mopeakande
 reviewer: msakande
-ms.custom: devplatv2, moe-wsvnet
+ms.custom: devplatv2
 ms.date: 08/15/2023
 ---
 
@@ -20,8 +20,6 @@ ms.date: 08/15/2023
 
 When deploying a machine learning model to a managed online endpoint, you can secure communication with the online endpoint by using [private endpoints](../private-link/private-endpoint-overview.md). In this article, you'll learn how a private endpoint can be used to secure inbound communication to a managed online endpoint. You'll also learn how a workspace managed virtual network can be used to provide secure communication between deployments and resources.
 
-[!INCLUDE [machine-learning-moe-with-workspace-vnet-preview](includes/machine-learning-moe-with-workspace-vnet-preview.md)]
-
 You can secure inbound scoring requests from clients to an _online endpoint_ and secure outbound communications between a _deployment_, the Azure resources it uses, and private resources. Security for inbound and outbound communication are configured separately. For more information on endpoints and deployments, see [What are endpoints and deployments](concept-endpoints-online.md).
 
 The following architecture diagram shows how communications flow through private endpoints to the managed online endpoint. Incoming scoring requests from a client's virtual network flow through the workspace's private endpoint to the managed online endpoint. Outbound communications from deployments to services are handled through private endpoints from the workspace's managed virtual network to those service instances.

articles/machine-learning/how-to-manage-workspace.md

@@ -10,7 +10,7 @@ author: deeikele
 ms.reviewer: sgilley
 ms.date: 09/21/2022
 ms.topic: how-to
-ms.custom: fasttrack-edit, FY21Q4-aml-seo-hack, contperf-fy21q4, sdkv2, event-tier1-build-2022, ignite-2022, devx-track-python, moe-wsvnet
+ms.custom: fasttrack-edit, FY21Q4-aml-seo-hack, contperf-fy21q4, sdkv2, event-tier1-build-2022, ignite-2022, devx-track-python
 ---
 
 # Manage Azure Machine Learning workspaces in the portal or with the Python SDK (v2)
@@ -53,7 +53,7 @@ As your needs change or requirements for automation increase you can also manage
 
 [!INCLUDE [register-namespace](includes/machine-learning-register-namespace.md)]
 
-* When you use network isolation that is based on a workspace's managed virtual network (preview) with a deployment, you can use resources (Azure Container Registry (ACR), Storage account, Key Vault, and Application Insights) from a different resource group or subscription than that of your workspace. However, these resources must belong to the same tenant as your workspace. For limitations that apply to securing managed online endpoints using a workspace's managed virtual network, see [Network isolation with managed online endpoints](concept-secure-online-endpoint.md#limitations).
+* When you use network isolation that is based on a workspace's managed virtual network with a deployment, you can use resources (Azure Container Registry (ACR), Storage account, Key Vault, and Application Insights) from a different resource group or subscription than that of your workspace. However, these resources must belong to the same tenant as your workspace. For limitations that apply to securing managed online endpoints using a workspace's managed virtual network, see [Network isolation with managed online endpoints](concept-secure-online-endpoint.md#limitations).
 
 * By default, creating a workspace also creates an Azure Container Registry (ACR).  Since ACR doesn't currently support unicode characters in resource group names, use a resource group that doesn't contain these characters.
 

articles/machine-learning/how-to-network-isolation-planning.md

@@ -10,7 +10,7 @@ ms.author: jhirono
 author: jhirono
 ms.date: 02/14/2023
 ms.topic: how-to
-ms.custom: build-2023, moe-wsvnet
+ms.custom: build-2023
 ---
 
 # Plan for network isolation

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

@@ -10,16 +10,14 @@ ms.reviewer: mopeakande
 author: dem108
 ms.author: sehan
 ms.date: 08/18/2023
-ms.custom: event-tier1-build-2022, devx-track-azurecli, moe-wsvnet
+ms.custom: event-tier1-build-2022, devx-track-azurecli
 ---
 
 # Secure your managed online endpoints with network isolation
 
 [!INCLUDE [machine-learning-dev-v2](includes/machine-learning-dev-v2.md)]
 
-In this article, you'll use network isolation to secure a managed online endpoint. You'll create a managed online endpoint that uses an Azure Machine Learning workspace's private endpoint for secure inbound communication. You'll also configure the workspace with a **managed virtual network** that **allows only approved outbound** communication for deployments (preview). Finally, you'll create a deployment that uses the private endpoints of the workspace's managed virtual network for outbound communication.
-
-[!INCLUDE [machine-learning-moe-with-workspace-vnet-preview](includes/machine-learning-moe-with-workspace-vnet-preview.md)]
+In this article, you'll use network isolation to secure a managed online endpoint. You'll create a managed online endpoint that uses an Azure Machine Learning workspace's private endpoint for secure inbound communication. You'll also configure the workspace with a **managed virtual network** that **allows only approved outbound** communication for deployments. Finally, you'll create a deployment that uses the private endpoints of the workspace's managed virtual network for outbound communication.
 
 For examples that use the legacy method for network isolation, see the deployment files [deploy-moe-vnet-legacy.sh](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-moe-vnet-legacy.sh) (for deployment using a generic model) and [deploy-moe-vnet-mlflow-legacy.sh](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-moe-vnet-mlflow-legacy.sh) (for deployment using an MLflow model) in the azureml-examples GitHub repo.
 
@@ -89,7 +87,7 @@ The commands in this tutorial are in the file `deploy-managed-online-endpoint-wo
 
 To create a secured managed online endpoint, create the endpoint in your workspace and set the endpoint's `public_network_access` to `disabled` to control inbound communication. The endpoint will then have to use the workspace's private endpoint for inbound communication.
 
-Because the workspace is configured to have a managed virtual network, any deployments of the endpoint will use the private endpoints of the managed virtual network for outbound communication (preview).
+Because the workspace is configured to have a managed virtual network, any deployments of the endpoint will use the private endpoints of the managed virtual network for outbound communication.
 
 1. Set the endpoint's name.
 

articles/machine-learning/includes/machine-learning-moe-with-workspace-vnet-preview.md

@@ -6,7 +6,7 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: mlops
 ms.topic: reference
-ms.custom: cliv2, event-tier1-build-2022, build-2023, moe-wsvnet
+ms.custom: cliv2, event-tier1-build-2022, build-2023
 author: dem108
 ms.author: sehan
 ms.date: 01/24/2023
@@ -46,7 +46,7 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `request_settings` | object | Scoring request settings for the deployment. See [RequestSettings](#requestsettings) for the set of configurable properties. | | |
 | `liveness_probe` | object | Liveness probe settings for monitoring the health of the container regularly. See [ProbeSettings](#probesettings) for the set of configurable properties. | | |
 | `readiness_probe` | object | Readiness probe settings for validating if the container is ready to serve traffic. See [ProbeSettings](#probesettings) for the set of configurable properties. | | |
-| `egress_public_network_access` | string |**Note:** This key is applicable when you use the [legacy network isolation method](concept-secure-online-endpoint.md#secure-outbound-access-with-legacy-network-isolation-method) to secure outbound communication for a deployment. We strongly recommend that you secure outbound communication for deployments using [a workspace managed VNet](concept-secure-online-endpoint.md) (preview) instead. <br><br>This flag secures the deployment by restricting communication between the deployment and the Azure resources used by it. Set to `disabled` to ensure that the download of the model, code, and images needed by your deployment are secured with a private endpoint. This flag is applicable only for managed online endpoints. | `enabled`, `disabled` | `enabled` |
+| `egress_public_network_access` | string |**Note:** This key is applicable when you use the [legacy network isolation method](concept-secure-online-endpoint.md#secure-outbound-access-with-legacy-network-isolation-method) to secure outbound communication for a deployment. We strongly recommend that you secure outbound communication for deployments using [a workspace managed VNet](concept-secure-online-endpoint.md) instead. <br><br>This flag secures the deployment by restricting communication between the deployment and the Azure resources used by it. Set to `disabled` to ensure that the download of the model, code, and images needed by your deployment are secured with a private endpoint. This flag is applicable only for managed online endpoints. | `enabled`, `disabled` | `enabled` |
 
 ### RequestSettings