Merge pull request #251900 from Blackmist/managed-network-ga

revmoving preview in prep for GA

Author: JamesJBarnett

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

@@ -231,7 +231,7 @@ To learn how to configure autoscaling, see [How to autoscale online endpoints](h
 
 When deploying an ML model to a managed online endpoint, you can secure communication with the online endpoint by using [private endpoints](../private-link/private-endpoint-overview.md).
 
-You can configure security for inbound scoring requests and outbound communications with the workspace and other services separately. Inbound communications use the private endpoint of the Azure Machine Learning workspace. Outbound communications use private endpoints created for the workspace's managed virtual network (preview).
+You can configure security for inbound scoring requests and outbound communications with the workspace and other services separately. Inbound communications use the private endpoint of the Azure Machine Learning workspace. Outbound communications use private endpoints created for the workspace's managed virtual network.
 
 For more information, see [Network isolation with managed online endpoints](concept-secure-online-endpoint.md).
 

articles/machine-learning/concept-enterprise-security.md

@@ -77,19 +77,19 @@ For more information, see the following articles:
 
 ## Network security and isolation
 
-To restrict network access to Azure Machine Learning resources, you can use an [Azure Machine Learning managed virtual network](how-to-managed-network.md) (preview) or [Azure Virtual Network (VNet)](../virtual-network/virtual-networks-overview.md). Using a virtual network reduces the attack surface for your solution, and the chances of data exfiltration.
+To restrict network access to Azure Machine Learning resources, you can use an [Azure Machine Learning managed virtual network](how-to-managed-network.md) or [Azure Virtual Network (VNet)](../virtual-network/virtual-networks-overview.md). Using a virtual network reduces the attack surface for your solution, and the chances of data exfiltration.
 
 You don't have to pick one or the other. For example, you can use a managed virtual network to secure managed compute resources and an Azure Virtual Network for your unmanaged resources or to secure client access to the workspace.
 
-* __Azure Machine Learning managed virtual network__ (preview) provides a fully managed solution that enables network isolation for your workspace and managed compute resources. You can use private endpoints to secure communication with other Azure services, and can restrict outbound communications. The following managed compute resources are secured with a managed network:
+* __Azure Machine Learning managed virtual network__ provides a fully managed solution that enables network isolation for your workspace and managed compute resources. You can use private endpoints to secure communication with other Azure services, and can restrict outbound communications. The following managed compute resources are secured with a managed network:
 
     * Serverless compute (including Spark serverless)
     * Compute cluster
     * Compute instance
     * Managed online endpoints
     * Batch online endpoints
 
-    For more information, see [Azure Machine Learning managed virtual network](how-to-managed-network.md) (preview).
+    For more information, see [Azure Machine Learning managed virtual network](how-to-managed-network.md).
 
 * __Azure Virtual Networks__ provides a more customizable virtual network offering. However, you're responsible for configuration and management. You may need to use network security groups, user-defined routing, or a firewall to restrict outbound communication.
 

articles/machine-learning/how-to-managed-network-compute.md

@@ -1,5 +1,5 @@
 ---
-title: Managed computes in managed virtual network isolation (preview)
+title: Managed computes in managed virtual network isolation
 titleSuffix: Azure Machine Learning
 description: Use managed compute resources with managed virtual network isolation with Azure Machine Learning.
 services: machine-learning
@@ -14,7 +14,7 @@ ms.topic: how-to
 
 # Use managed compute in a managed virtual network
 
-Learn how to configure compute clusters or compute instances in an Azure Machine Learning managed virtual network (preview).
+Learn how to configure compute clusters or compute instances in an Azure Machine Learning managed virtual network.
 
 When using a managed network, compute resources managed by Azure Machine Learning can participate in the virtual network. Azure Machine Learning _compute clusters_, _compute instances_, and _managed online endpoints_ are created in the managed network.
 

articles/machine-learning/how-to-managed-network.md

@@ -19,7 +19,7 @@ In this article, you learn how to plan your network isolation for Azure Machine
 
 ## Recommended architecture (Managed Network Isolation pattern)
 
-[Using a Managed virtual network](how-to-managed-network.md) (preview) provides an easier configuration for network isolation. It automatically secures your workspace and managed compute resources in a managed virtual network. You can add private endpoint connections for other Azure services that the workspace relies on, such as Azure Storage Accounts. Depending on your needs, you can allow all outbound traffic to the public network or allow only the outbound traffic you approve. Outbound traffic required by the Azure Machine Learning service is automatically enabled for the managed virtual network. We recommend using workspace managed network isolation for a built-in friction less network isolation method. We have two patterns: allow internet outbound mode or allow only approved outbound mode.
+[Using a Managed virtual network](how-to-managed-network.md) provides an easier configuration for network isolation. It automatically secures your workspace and managed compute resources in a managed virtual network. You can add private endpoint connections for other Azure services that the workspace relies on, such as Azure Storage Accounts. Depending on your needs, you can allow all outbound traffic to the public network or allow only the outbound traffic you approve. Outbound traffic required by the Azure Machine Learning service is automatically enabled for the managed virtual network. We recommend using workspace managed network isolation for a built-in friction less network isolation method. We have two patterns: allow internet outbound mode or allow only approved outbound mode.
 
 ### Allow internet outbound mode
 
@@ -157,9 +157,7 @@ Azure Machine Learning uses a private endpoint to secure inbound communication t
 
 #### Outbound communication
 
-[!INCLUDE [machine-learning-preview-generic-disclaimer](includes/machine-learning-preview-generic-disclaimer.md)]
-
-To secure outbound communication from a deployment to resources, Azure Machine Learning uses a workspace managed virtual network (preview).  The deployment needs to be created in the workspace managed VNet so that it can use the private endpoints of the workspace managed virtual network for outbound communication.
+To secure outbound communication from a deployment to resources, Azure Machine Learning uses a workspace managed virtual network.  The deployment needs to be created in the workspace managed VNet so that it can use the private endpoints of the workspace managed virtual network for outbound communication.
 
 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 communication from deployments to services is handled through private endpoints from the workspace's managed virtual network to those service instances.
 

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

@@ -94,7 +94,7 @@ These prerequisites cover the submission of a Spark job from Azure Machine Learn
 > - To ensure successful execution of the Spark job, assign the **Contributor** and **Storage Blob Data Contributor** roles, on the Azure storage account used for data input and output, to the identity that the Spark job uses
 > - Public Network Access should be enabled in Azure Synapse workspace to ensure successful execution of the Spark job using an [attached Synapse Spark pool](./how-to-manage-synapse-spark-pool.md).
 > - If an [attached Synapse Spark pool](./how-to-manage-synapse-spark-pool.md) points to a Synapse Spark pool, in an Azure Synapse workspace that has a managed virtual network associated with it, [a managed private endpoint to storage account should be configured](../synapse-analytics/security/connect-to-a-secure-storage-account.md) to ensure data access.
-> - Serverless Spark compute supports Azure Machine Learning managed virtual network (preview). If a [managed network is provisioned for the serverless Spark compute, the corresponding private endpoints for the storage account should also be provisioned](./how-to-managed-network.md#configure-for-serverless-spark-jobs) to ensure data access.
+> - Serverless Spark compute supports Azure Machine Learning managed virtual network. If a [managed network is provisioned for the serverless Spark compute, the corresponding private endpoints for the storage account should also be provisioned](./how-to-managed-network.md#configure-for-serverless-spark-jobs) to ensure data access.
 
 ## Submit a standalone Spark job
 A Python script developed by [interactive data wrangling](./interactive-data-wrangling-with-apache-spark-azure-ml.md) can be used to submit a batch job to process a larger volume of data, after making necessary changes for Python script parameterization. A simple data wrangling batch job can be submitted as a standalone Spark job.