Merge pull request #241559 from Blackmist/review-feedback

incorporating feedback from reviewers

Author: prmerger-automator[bot]

articles/machine-learning/concept-machine-learning-registries-mlops.md

@@ -8,7 +8,7 @@ ms.subservice: mlops
 ms.author: mabables
 author: ManojBableshwar
 ms.reviewer: larryfr
-ms.date: 9/9/2022
+ms.date: 06/14/2023
 ms.topic: conceptual
 ms.custom: ignite-2022, build-2023
 ---
@@ -17,7 +17,7 @@ ms.custom: ignite-2022, build-2023
 
 In this article, you'll learn how to scale MLOps across development, testing and production environments. Your environments can vary from few to many based on the complexity of your IT environment and is influenced by factors such as:
 
-* Security and compliance policies - do production environments need to be isolated from development environments in terms of access controls, network architecture, data exposure, etc.?
+* Security and compliance policies - Do production environments need to be isolated from development environments in terms of access controls, network architecture, data exposure, etc.?
 * Subscriptions - Are your development environments in one subscription and production environments in a different  subscription? Often separate subscriptions are used to account for billing, budgeting, and cost management purposes.
 * Regions - Do you need to deploy to different Azure regions to support latency and redundancy requirements? 
 
@@ -42,6 +42,6 @@ The following diagram illustrates promotion of pipelines between exploratory and
 
 ## Next steps
 
-* [Create a registry](./how-to-manage-registries.md).
-* [Network isolation with registries](./how-to-registry-network-isolation.md).
-* [Share models, components, and environments using registries](./how-to-share-models-pipelines-across-workspaces-with-registries.md).
+* [Create a registry](./how-to-manage-registries.md)
+* [Network isolation with registries](./how-to-registry-network-isolation.md)
+* [Share models, components, and environments using registries](./how-to-share-models-pipelines-across-workspaces-with-registries.md)

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

@@ -15,8 +15,7 @@ ms.custom: build-2023
 
 # Workspace managed network isolation (preview)
 
-[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
-[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
+[!INCLUDE [dev v2](../../includes/machine-learning-dev-v2.md)]
 
 Azure Machine Learning provides preview support for managed virtual network (VNet) isolation. Managed VNet isolation streamlines and automates your network isolation configuration with a built-in, workspace-level Azure Machine Learning managed virtual network.
 
@@ -80,7 +79,7 @@ Before following the steps in this article, make sure you have the following pre
 
 * The Azure CLI examples in this article use `ws` to represent the name of the workspace, and `rg` to represent the name of the resource group. Change these values as needed when using the commands with your Azure subscription.
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 * An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/).
 
@@ -201,7 +200,7 @@ You can configure a managed VNet using either the `az ml workspace create` or `a
         type: private_endpoint
     ```
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 To configure a managed VNet that allows internet outbound communications, use the `ManagedNetwork` class to define a network with `IsolationMode.ALLOW_INTERNET_OUTBOUND`. You can then use the `ManagedNetwork` object to create a new workspace or update an existing one. To define _outbound rules_ to Azure services that the workspace relies on, use the `PrivateEndpointDestination` class to define a new private endpoint to the service.
 
@@ -396,7 +395,7 @@ You can configure a managed VNet using either the `az ml workspace create` or `a
         type: private_endpoint
     ```
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 To configure a managed VNet that allows only approved outbound communications, use the `ManagedNetwork` class to define a network with `IsolationMode.ALLOw_ONLY_APPROVED_OUTBOUND`. You can then use the `ManagedNetwork` object to create a new workspace or update an existing one. To define _outbound rules_ to Azure services that the workspace relies on, use the `PrivateEndpointDestination` class to define a new private endpoint to the service.
 
@@ -541,7 +540,7 @@ To configure a managed VNet that allows only approved outbound communications, u
 ## Configure for serverless spark jobs
 
 > [!TIP]
-> The steps in this section are only needed for Spark serverless. If you are using [serverless compute cluster](how-to-use-serverless-compute.md), you can skip this section.
+> The steps in this section are only needed for __Spark serverless__. If you are using [serverless __compute cluster__](how-to-use-serverless-compute.md), you can skip this section.
 
 To enable the [serverless spark jobs](how-to-submit-spark-jobs.md) for the managed VNet, you must perform the following actions:
 
@@ -576,7 +575,7 @@ To enable the [serverless spark jobs](how-to-submit-spark-jobs.md) for the manag
     az ml workspace update --file workspace_pe.yml --resource_group rg --name ws
     ```
 
-    # [Python](#tab/python)
+    # [Python SDK](#tab/python)
 
     The following example demonstrates how to create a managed VNet for an existing Azure Machine Learning workspace named `myworkspace`. It also adds a private endpoint for the Azure Storage Account and sets `spark_enabled=true`:
 
@@ -633,7 +632,7 @@ To enable the [serverless spark jobs](how-to-submit-spark-jobs.md) for the manag
     az ml workspace provision-network -g my_resource_group -n my_workspace_name --include-spark
     ```
 
-    # [Python](#tab/python)
+    # [Python SDK](#tab/python)
 
     The following example shows how to provision a managed VNet for serverless spark jobs:
 
@@ -675,7 +674,7 @@ To remove an outbound rule from the managed VNet, use the following command:
 az ml workspace outbound-rule remove --rule rule-name --workspace-name ws --resource-group rg
 ```
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 The following example demonstrates how to manage outbound rules for a workspace named `myworkspace`:
 
@@ -736,4 +735,4 @@ Currently we don't have any recommended outbound rules.
 
 ## Next steps
 
-* [Troubleshoot managed virtual network](how-to-troubleshoot-managed-network.md).
+* [Troubleshoot managed virtual network](how-to-troubleshoot-managed-network.md)