v1 sdk deprecation notes

Blackmist · Blackmist · commit 1e67b04c1509 · 2025-01-10T13:22:55.000-05:00
diff --git a/articles/machine-learning/concept-data-encryption.md b/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)]
diff --git a/articles/machine-learning/concept-ml-pipelines.md b/articles/machine-learning/concept-ml-pipelines.md
@@ -17,6 +17,8 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 
 :::moniker range="azureml-api-1"
 [!INCLUDE [dev v1](includes/machine-learning-dev-v1.md)]
+
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
 :::moniker-end
 :::moniker range="azureml-api-2"
 [!INCLUDE [dev v2](includes/machine-learning-dev-v2.md)]
diff --git a/articles/machine-learning/concept-soft-delete.md b/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.
diff --git a/articles/machine-learning/concept-workspace.md b/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.
 
diff --git a/articles/machine-learning/how-to-change-storage-access-key.md b/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.
diff --git a/articles/machine-learning/how-to-export-delete-data.md b/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:
 
diff --git a/articles/machine-learning/how-to-network-security-overview.md b/articles/machine-learning/how-to-network-security-overview.md
@@ -23,6 +23,8 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 :::moniker-end
 :::moniker range="azureml-api-1"
 [!INCLUDE [dev v1](includes/machine-learning-dev-v1.md)]
+
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
 :::moniker-end
 
 [!INCLUDE [managed-vnet-note](includes/managed-vnet-note.md)]
diff --git a/articles/machine-learning/how-to-setup-customer-managed-keys.md b/articles/machine-learning/how-to-setup-customer-managed-keys.md
@@ -138,6 +138,10 @@ For more information on customer-managed keys with Azure Cosmos DB, see [Configu
 > [!IMPORTANT]
 > Deploying to Azure Container Instances is not available in SDK or CLI v2. Only through SDK & CLI v1.
 
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [v1 cli deprecation](includes/machine-learning-cli-v1-deprecation.md)]
+
 When __deploying__ a trained model to an Azure Container instance (ACI), you can encrypt the deployed resource using a customer-managed key. For information on generating a key, see [Encrypt data with a customer-managed key](/azure/container-instances/container-instances-encrypt-data#generate-a-new-key).
 
 To use the key when deploying a model to Azure Container Instance, create a new deployment configuration using `AciWebservice.deploy_configuration()`. Provide the key information using the following parameters:
diff --git a/articles/machine-learning/how-to-troubleshoot-environments.md b/articles/machine-learning/how-to-troubleshoot-environments.md
@@ -21,6 +21,8 @@ monikerRange: 'azureml-api-1 || azureml-api-2'
 :::moniker-end
 :::moniker range="azureml-api-1"
 [!INCLUDE [dev v1](includes/machine-learning-dev-v1.md)]
+
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
 :::moniker-end
 
 In this article, learn how to troubleshoot common problems you may encounter with environment image builds and learn about AzureML environment vulnerabilities.
diff --git a/articles/machine-learning/how-to-use-event-grid.md b/articles/machine-learning/how-to-use-event-grid.md
@@ -185,6 +185,10 @@ Use [Azure Logic Apps](/azure/logic-apps/) to configure emails for all your even
 > [!IMPORTANT]
 > This example relies on a feature (data drift) that is only available when using Azure Machine Learning SDK v1 or Azure CLI extension v1 for Azure Machine Learning. For more information, see [What is Azure Machine Learning CLI & SDK v2](concept-v2.md).
 
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [v1 cli deprecation](includes/machine-learning-cli-v1-deprecation.md)]
+
 Models go stale over time, and not remain useful in the context it's running in. One way to tell if it's time to retrain the model is detecting data drift. 
 
 This example shows how to use Event Grid with an Azure Logic App to trigger retraining. The example triggers an Azure Data Factory pipeline when data drift occurs between a model's training and serving datasets.
diff --git a/articles/machine-learning/how-to-workspace-diagnostic-api.md b/articles/machine-learning/how-to-workspace-diagnostic-api.md
@@ -21,6 +21,8 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 :::moniker-end
 :::moniker range="azureml-api-1"
 [!INCLUDE [sdk v1](includes/machine-learning-sdk-v1.md)]
+
+[!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
 :::moniker-end
 
 Azure Machine Learning provides a diagnostic API that can be used to identify problems with your workspace. Errors returned in the diagnostics report include information on how to resolve the problem.
