Merge pull request #3798 from MicrosoftDocs/ml-sdkv1-deprecation

[Do not merge] Ml sdkv1 deprecation -> main -- 03/31 - 10:00 AM PST

Author: Albertyang0

articles/machine-learning/concept-data-encryption.md

@@ -97,6 +97,10 @@ For examples of creating a workspace by using an existing container registry, se
 > [!IMPORTANT]
 > Deployments to Azure Container Instances rely on the Azure Machine Learning Python SDK and CLI v1.
 
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [v1 cli deprecation](includes/machine-learning-cli-v1-deprecation.md)]
+
 You can encrypt a deployed Azure Container Instances resource by using customer-managed keys. The customer-managed keys that you use for Container Instances can be stored in the key vault for your workspace.
 
 [!INCLUDE [sdk v1](includes/machine-learning-sdk-v1.md)]

articles/machine-learning/concept-ml-pipelines.md

@@ -18,7 +18,9 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 :::moniker range="azureml-api-1"
 [!INCLUDE [dev v1](includes/machine-learning-dev-v1.md)]
 
-[!INCLUDE [cli-version-info](./includes/machine-learning-cli-v1-deprecation.md)]
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [cli v1 deprecation](./includes/machine-learning-cli-v1-deprecation.md)]
 :::moniker-end
 :::moniker range="azureml-api-2"
 [!INCLUDE [dev v2](includes/machine-learning-dev-v2.md)]

articles/machine-learning/concept-soft-delete.md

@@ -69,6 +69,10 @@ When deleting a workspace from the Azure portal, check __Delete the workspace pe
 > [!TIP]
 > The v1 SDK and CLI don't provide functionality to override the default soft-delete behavior. To override the default behavior from SDK or CLI, use the v2 versions. For more information, see the [CLI & SDK v2](concept-v2.md) article or the [v2 version of this article](concept-soft-delete.md?view=azureml-api-2&preserve-view=true#deleting-a-workspace).
 
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [v1 cli deprecation](includes/machine-learning-cli-v1-deprecation.md)]
+
 :::moniker-end  
 :::moniker range="azureml-api-2"
 If you're using the [Azure Machine Learning SDK or CLI](/python/api/azure-ai-ml/azure.ai.ml.operations.workspaceoperations#azure-ai-ml-operations-workspaceoperations-begin-delete), you can set the `permanently_delete` flag.

articles/machine-learning/concept-v2.md

@@ -92,23 +92,21 @@ SDK v2 is useful in the following scenarios:
 
 ## Should I use v1 or v2?
 
-Support for CLI v1 will end on September 30, 2025.  
+Support for CLI v1 will end on September 30, 2025. Support for SDK v1 will end on June 30, 2026. You can continue to use CLI v1 and SDK v1 until those dates. However, we recommend that you transition to CLI v2 and SDK v2 before those dates.
 
 We encourage you to migrate your code for both CLI and SDK v1 to CLI and SDK v2. For more information, see [Upgrade to v2](how-to-migrate-from-v1.md).
 
 ### CLI v2
 
 Azure Machine Learning CLI v1 has been deprecated. Support for the v1 extension will end on September 30, 2025. You'll be able to install and use the v1 extension until that date.
 
-We recommend that you transition to the `ml`, or v2, extension before September 30, 2025.
+We recommend that you transition to the `ml`, or v2, extension before September 30, 2025. For more information, see [Upgrade to v2](how-to-migrate-from-v1.md).
 
 ### SDK v2
 
-Azure Machine Learning Python SDK v1 doesn't have a planned deprecation date. If you have significant investments in Python SDK v1 and don't need any new features offered by SDK v2, you can continue to use SDK v1. However, you should consider using SDK v2 if:
+Support for the Azure Machine Learning SDK v1 will end on June 30, 2026. You'll be able to install and use the SDK v1 until that date.
 
-* You want to use new features like reusable components and managed inferencing.
-* You're starting a new workflow or pipeline. All new features and future investments will be introduced in v2.
-* You want to take advantage of the improved usability of the Python SDK v2 ability to compose jobs and pipelines by using Python functions, with easy evolution from simple to complex tasks.
+We recommend that you transition to the SDK v2 before June 30, 2026. For more information, see [Upgrade to v2](how-to-migrate-from-v1.md).
 
 ## Related content
 

articles/machine-learning/concept-workspace.md

@@ -101,31 +101,45 @@ There are multiple ways to create a workspace. To get started, use one of the fo
 * Use the [VS Code extension](how-to-manage-resources-vscode.md#create-a-workspace) if you work in Visual Studio Code.
 
 To automate workspace creation using your preferred security settings:
-* [Azure Resource Manager / Bicep templates](how-to-create-workspace-template.md) provide a declarative syntax to deploy Azure resources. An alternative option is to use [Terraform](how-to-manage-workspace-terraform.md). Also see the [Bicep template](/samples/azure/azure-quickstart-templates/machine-learning-end-to-end-secure/) or [Terraform template](https://github.com/Azure/terraform/tree/master/quickstart/201-machine-learning-moderately-secure).
+
 :::moniker range="azureml-api-2"
+* [Azure Resource Manager / Bicep templates](how-to-create-workspace-template.md) provide a declarative syntax to deploy Azure resources. An alternative option is to use [Terraform](how-to-manage-workspace-terraform.md). Also see the [Bicep template](/samples/azure/azure-quickstart-templates/machine-learning-end-to-end-secure/) or [Terraform template](https://github.com/Azure/terraform/tree/master/quickstart/201-machine-learning-moderately-secure).
 * Use the [Azure Machine Learning CLI](how-to-configure-cli.md) or [Azure Machine Learning SDK for Python](how-to-manage-workspace.md?tabs=python#create-a-workspace) for prototyping and as part of your [MLOps workflows](concept-model-management-and-deployment.md).
+* Use [REST APIs](how-to-manage-rest.md) directly in scripting environment, for platform integration or in MLOps workflows.
 :::moniker-end
 :::moniker range="azureml-api-1"
-* Use the [Azure Machine Learning CLI](./v1/reference-azure-machine-learning-cli.md) or [Azure Machine Learning SDK for Python](how-to-manage-workspace.md?tabs=python#create-a-workspace) for prototyping and as part of your [MLOps workflows](concept-model-management-and-deployment.md).
-:::moniker-end
+* [Azure Resource Manager / Bicep templates](how-to-create-workspace-template.md) provide a declarative syntax to deploy Azure resources. An alternative option is to use [Terraform](how-to-manage-workspace-terraform.md). Also see the [Bicep template](/samples/azure/azure-quickstart-templates/machine-learning-end-to-end-secure/) or [Terraform template](https://github.com/Azure/terraform/tree/master/quickstart/201-machine-learning-moderately-secure).
+* Use the [Azure Machine Learning CLI v1](./v1/reference-azure-machine-learning-cli.md) or [Azure Machine Learning SDK v1 for Python](how-to-manage-workspace.md?tabs=python#create-a-workspace) for prototyping and as part of your [MLOps workflows](concept-model-management-and-deployment.md).
+
+    [!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+    [!INCLUDE [v1 cli deprecation](includes/machine-learning-cli-v1-deprecation.md)]
+
 * Use [REST APIs](how-to-manage-rest.md) directly in scripting environment, for platform integration or in MLOps workflows.
+:::moniker-end
 
 ## Tools for workspace interaction and management
 
 Once your workspace is set up, you can interact with it in the following ways:
 
+:::moniker range="azureml-api-2"
 + On the web:
     + [Azure Machine Learning studio ](https://ml.azure.com) 
     + [Azure Machine Learning designer](concept-designer.md) 
-:::moniker range="azureml-api-2"
 + In any Python environment with the [Azure Machine Learning SDK](https://aka.ms/sdk-v2-install).
 + On the command line, using the Azure Machine Learning [CLI extension v2](how-to-configure-cli.md)
++ [Azure Machine Learning VS Code Extension](how-to-manage-resources-vscode.md#workspaces)
 :::moniker-end
 :::moniker range="azureml-api-1"
-+ In any Python environment with the [Azure Machine Learning SDK](/python/api/overview/azure/ml/)
++ On the web:
+    + [Azure Machine Learning studio ](https://ml.azure.com) 
+    + [Azure Machine Learning designer](concept-designer.md) 
++ In any Python environment with the [Azure Machine Learning SDK v1](/python/api/overview/azure/ml/)
+    [!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
 + On the command line, using the Azure Machine Learning [CLI extension v1](./v1/reference-azure-machine-learning-cli.md)
-:::moniker-end
+    [!INCLUDE [v1 cli deprecation](includes/machine-learning-cli-v1-deprecation.md)]
 + [Azure Machine Learning VS Code Extension](how-to-manage-resources-vscode.md#workspaces)
+:::moniker-end
 
 The following workspace management tasks are available in each interface.
 

articles/machine-learning/how-to-change-storage-access-key.md

@@ -22,6 +22,10 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 :::moniker range="azureml-api-1"
 [!INCLUDE [cli v1](includes/machine-learning-dev-v1.md)]
 [!INCLUDE [SDK v1](includes/machine-learning-sdk-v1.md)]
+
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [v1 cli deprecation](includes/machine-learning-cli-v1-deprecation.md)]
 :::moniker-end
 
 Learn how to change the access keys for Azure Storage accounts used by Azure Machine Learning. Azure Machine Learning can use storage accounts to store data or trained models.

articles/machine-learning/how-to-configure-network-isolation-with-v2.md

@@ -10,7 +10,7 @@ ms.topic: how-to
 ms.author: larryfr
 author: Blackmist
 ms.reviewer: meerakurup
-ms.date: 02/05/2025
+ms.date: 03/28/2025
 ---
 
 # Network Isolation Change with Our New API Platform on Azure Resource Manager
@@ -21,6 +21,9 @@ In this article, you'll learn about network isolation changes with our new v2 AP
 
 There are two types of operations used by the v1 and v2 APIs, __Azure Resource Manager (ARM)__ and __Azure Machine Learning workspace__.
 
+> [!IMPORTANT]
+> The v1 API is deprecated as of March 31, 2025. Support for using the CLI v1 to access this API will end on September 30, 2025. Support for using the SDK v1 to access this API will end on June 30, 2026.
+
 With the v1 API, most operations used the workspace. For v2, we've moved most operations to use public ARM.
 
 | API version | Public ARM | Inside workspace virtual network |

articles/machine-learning/how-to-export-delete-data.md

@@ -110,7 +110,9 @@ Select **Download all** to start the model download process, as shown in this sc
 
 :::moniker range="azureml-api-1"
 
-## Export and delete resources using the Python SDK
+## Export and delete resources using the Python SDK v1
+
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
 
 You can download the outputs of a particular job using:
 

articles/machine-learning/how-to-migrate-from-v1.md

@@ -22,6 +22,8 @@ Azure Machine Learning's v2 REST APIs, Azure CLI extension, and Python SDK intro
 > [!IMPORTANT]
 > The designations "v1" and "v2" refer to the API, SDK, and CLI extension used by clients to interact with the service, and not the Azure Machine Learning service itself. There is no upgrade process for the service or your existing workspaces, only for your client code. Your Azure Machine Learning workspaces can be used with both the v1 and v2 APIs. However, new features will only be available through the v2 APIs.
 >
+> Support for the CLI v1 will end on September 30, 2025 and support for the Python SDK v1 will end on June 30, 2026.
+>
 > For more information on v2, see [what is v2](concept-v2.md?view=azureml-api-2&preserve-view=true). For a mapping of differences between v1 and v2 SDKs, with links to articles with example code, see [Mapping of Python SDK v1 to v2](#mapping-of-python-sdk-v1-to-v2).
 
 ## Prerequisites

articles/machine-learning/how-to-network-security-overview.md

@@ -24,7 +24,9 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 :::moniker range="azureml-api-1"
 [!INCLUDE [dev v1](includes/machine-learning-dev-v1.md)]
 
-[!INCLUDE [cli-version-info](./includes/machine-learning-cli-v1-deprecation.md)]
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [cli v1 deprecation](./includes/machine-learning-cli-v1-deprecation.md)]
 :::moniker-end
 
 [!INCLUDE [managed-vnet-note](includes/managed-vnet-note.md)]