adding deprecation note

Blackmist · Blackmist · commit f439d33e5d4e · 2025-01-08T10:24:58.000-05:00
diff --git a/articles/machine-learning/v1/how-to-data-ingest-adf.md b/articles/machine-learning/v1/how-to-data-ingest-adf.md
@@ -18,6 +18,8 @@ ms.custom: UpdateFrequency5, devx-track-python, data4ml, sdkv1
 
 # Data ingestion with Azure Data Factory
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn about the available options for building a data ingestion pipeline with [Azure Data Factory](/azure/data-factory/introduction). This Azure Data Factory pipeline is used to ingest data for use with [Azure Machine Learning](../overview-what-is-azure-machine-learning.md). Data Factory allows you to easily extract, transform, and load (ETL) data. Once the data is transformed and loaded into storage, it can be used to train your machine learning models in Azure Machine Learning.
 
 Simple data transformation can be handled with native Data Factory activities and instruments such as [data flow](/azure/data-factory/control-flow-execute-data-flow-activity). When it comes to more complicated scenarios, the data can be processed with some custom code. For example, Python or R code.
diff --git a/articles/machine-learning/v1/how-to-data-prep-synapse-spark-pool.md b/articles/machine-learning/v1/how-to-data-prep-synapse-spark-pool.md
@@ -18,6 +18,8 @@ ms.custom: UpdateFrequency5, data4ml, synapse-azureml, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 > [!WARNING]
 > The Azure Synapse Analytics integration with Azure Machine Learning, available in Python SDK v1, is deprecated. Users can still use Synapse workspace, registered with Azure Machine Learning, as a linked service. However, a new Synapse workspace can no longer be registered with Azure Machine Learning as a linked service. We recommend use of serverless Spark compute and attached Synapse Spark pools, available in CLI v2 and Python SDK v2. For more information, visit [https://aka.ms/aml-spark](https://aka.ms/aml-spark).
 
diff --git a/articles/machine-learning/v1/how-to-debug-parallel-run-step.md b/articles/machine-learning/v1/how-to-debug-parallel-run-step.md
@@ -19,6 +19,8 @@ ms.date: 11/16/2022
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to troubleshoot when you get errors using the [ParallelRunStep](/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallel_run_step.parallelrunstep) class from the [Azure Machine Learning SDK](/python/api/overview/azure/ml/intro).
 
 For general tips on troubleshooting a pipeline, see [Troubleshooting machine learning pipelines](how-to-debug-pipelines.md).
diff --git a/articles/machine-learning/v1/how-to-debug-pipelines.md b/articles/machine-learning/v1/how-to-debug-pipelines.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, troubleshooting, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to troubleshoot when you get errors running a [machine learning pipeline](../concept-ml-pipelines.md) in the [Azure Machine Learning SDK](/python/api/overview/azure/ml/intro) and [Azure Machine Learning designer](concept-designer.md). 
 
 ## Troubleshooting tips
diff --git a/articles/machine-learning/v1/how-to-debug-visual-studio-code.md b/articles/machine-learning/v1/how-to-debug-visual-studio-code.md
@@ -18,6 +18,8 @@ monikerRange: 'azureml-api-1'
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Learn how to interactively debug Azure Machine Learning experiments, pipelines, and deployments using Visual Studio Code (VS Code) and [debugpy](https://github.com/microsoft/debugpy/).
 
 ## Run and debug experiments locally
diff --git a/articles/machine-learning/v1/how-to-deploy-advanced-entry-script.md b/articles/machine-learning/v1/how-to-deploy-advanced-entry-script.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, deploy, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article explains how to write entry scripts for specialized use cases.
 
 ## Prerequisites
diff --git a/articles/machine-learning/v1/how-to-deploy-and-where.md b/articles/machine-learning/v1/how-to-deploy-and-where.md
@@ -19,6 +19,8 @@ adobe-target: true
 
 [!INCLUDE [sdk & cli v1](../includes/machine-learning-dev-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Learn how to deploy your machine learning or deep learning model as a web service in the Azure cloud.
 
 [!INCLUDE [endpoints-option](../includes/machine-learning-endpoints-preview-note.md)]
diff --git a/articles/machine-learning/v1/how-to-deploy-azure-container-instance.md b/articles/machine-learning/v1/how-to-deploy-azure-container-instance.md
@@ -15,6 +15,10 @@ ms.date: 07/10/2024
 
 # Deploy a model to Azure Container Instances with CLI (v1)
 
+[!INCLUDE [cli v1 deprecation](../includes/machine-learning-cli-v1-deprecation.md)]
+
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 > [!IMPORTANT]
 > This article shows how to use the CLI and SDK v1 to deploy a model.  For the recommended approach for v2, see [Deploy and score a machine learning model by using an online endpoint](/azure/machine-learning/how-to-deploy-managed-online-endpoints).
 
@@ -38,8 +42,6 @@ For information on quota and region availability for ACI, see [Quotas and region
 
 - The [Azure CLI extension (v1) for Machine Learning service](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro), or the [Azure Machine Learning Visual Studio Code extension](../how-to-setup-vs-code.md).
 
-    [!INCLUDE [cli v1 deprecation](../includes/machine-learning-cli-v1-deprecation.md)]
-
 - The __Python__ code snippets in this article assume that the following variables are set:
 
     * `ws` - Set to your workspace.
diff --git a/articles/machine-learning/v1/how-to-deploy-azure-kubernetes-service.md b/articles/machine-learning/v1/how-to-deploy-azure-kubernetes-service.md
@@ -16,6 +16,8 @@ ms.date: 09/09/2024
 
 # Deploy a model to an Azure Kubernetes Service cluster with v1
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 > [!IMPORTANT]
 > This article explains how to use the Azure Machine Learning CLI (v1) and Azure Machine Learning SDK for Python (v1) to deploy a model. For the recommended approach for v2, see [Deploy and score a machine learning model by using an online endpoint](/azure/machine-learning/how-to-deploy-managed-online-endpoints).
 
diff --git a/articles/machine-learning/v1/how-to-deploy-inferencing-gpus.md b/articles/machine-learning/v1/how-to-deploy-inferencing-gpus.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, deploy, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article teaches you how to use Azure Machine Learning to deploy a GPU-enabled model as a web service. The information in this article is based on deploying a model on Azure Kubernetes Service (AKS). The AKS cluster provides a GPU resource that is used by the model for inference.
 
 Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.
diff --git a/articles/machine-learning/v1/how-to-deploy-local-container-notebook-vm.md b/articles/machine-learning/v1/how-to-deploy-local-container-notebook-vm.md
@@ -14,6 +14,8 @@ ms.date: 04/22/2021
 
 # Deploy a model locally
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Learn how to use Azure Machine Learning to deploy a model as a web service on your Azure Machine Learning compute instance. Use compute instances if one of the following conditions is true:
 
 - You need to quickly deploy and validate your model.
diff --git a/articles/machine-learning/v1/how-to-deploy-local.md b/articles/machine-learning/v1/how-to-deploy-local.md
@@ -16,6 +16,8 @@ ms.custom: UpdateFrequency5, deploy, sdkv1, build-2023
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article describes how to use your local computer as a target for training or deploying models created in Azure Machine Learning. Azure Machine Learning is flexible enough to work with most Python machine learning frameworks. Machine learning solutions generally have complex dependencies that can be difficult to duplicate. This article will show you how to balance total control with ease of use.
 
 Scenarios for local deployment include:
diff --git a/articles/machine-learning/v1/how-to-deploy-mlflow-models.md b/articles/machine-learning/v1/how-to-deploy-mlflow-models.md
@@ -19,6 +19,8 @@ ms.custom: UpdateFrequency5, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 MLflow is an open-source library for managing the life cycle of machine learning experiments. MLflow integration with Azure Machine Learning lets you extend the management capabilities beyond model training to the deployment phase of production models. In this article, you deploy an [MLflow](https://www.mlflow.org/) model as an Azure web service and apply Azure Machine Learning model management and data drift detection features to your production models.
 
 The following diagram demonstrates how the MLflow deploy API integrates with Azure Machine Learning to deploy models. You create models as Azure web services by using popular frameworks like PyTorch, Tensorflow, or scikit-learn, and manage the services in your workspace:
diff --git a/articles/machine-learning/v1/how-to-deploy-model-cognitive-search.md b/articles/machine-learning/v1/how-to-deploy-model-cognitive-search.md
@@ -23,6 +23,8 @@ monikerRange: 'azureml-api-1'
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article teaches you how to use Azure Machine Learning to deploy a model for use with [Azure AI Search](/azure/search/search-what-is-azure-search).
 
 Azure AI Search performs content processing over heterogenous content, to make it queryable by humans or applications. This process can be enhanced by using a model deployed from Azure Machine Learning.
diff --git a/articles/machine-learning/v1/how-to-deploy-model-designer.md b/articles/machine-learning/v1/how-to-deploy-model-designer.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, deploy, studio, designer
 
 # Use the studio to deploy models trained in the designer
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to deploy a designer model as an online (real-time) endpoint in Azure Machine Learning studio.
 
 Once registered or downloaded, you can use designer trained models just like any other model. Exported models can be deployed in use cases such as internet of things (IoT) and local deployments.
diff --git a/articles/machine-learning/v1/how-to-deploy-package-models.md b/articles/machine-learning/v1/how-to-deploy-package-models.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, deploy
 
 # How to package a registered model with Docker
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article shows how to package a registered Azure Machine Learning model with Docker.
 
 ## Prerequisites
diff --git a/articles/machine-learning/v1/how-to-deploy-pipelines.md b/articles/machine-learning/v1/how-to-deploy-pipelines.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, sdkv1, devx-track-python
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article will show you how to share a machine learning pipeline with your colleagues or customers.
 
 Machine learning pipelines are reusable workflows for machine learning tasks. One benefit of pipelines is increased collaboration. You can also version pipelines, allowing customers to use the current model while you're working on a new version. 
diff --git a/articles/machine-learning/v1/how-to-deploy-profile-model.md b/articles/machine-learning/v1/how-to-deploy-profile-model.md
@@ -18,6 +18,8 @@ ms.custom: UpdateFrequency5, deploy, cliv1, sdkv1
 
 [!INCLUDE [dev v1](../includes/machine-learning-dev-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article shows how to profile a machine learning to model to determine how much CPU and memory you will need to allocate for the model when deploying it as a web service.
 
 > [!IMPORTANT]
diff --git a/articles/machine-learning/v1/how-to-deploy-update-web-service.md b/articles/machine-learning/v1/how-to-deploy-update-web-service.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, deploy, cliv1, sdkv1
 
 [!INCLUDE [dev v1](../includes/machine-learning-dev-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to update a web service that was deployed with Azure Machine Learning.
 
 ## Prerequisites
diff --git a/articles/machine-learning/v1/how-to-designer-import-data.md b/articles/machine-learning/v1/how-to-designer-import-data.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, designer
 
 # Import data into Azure Machine Learning designer
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to import your own data in the designer to create custom solutions. There are two ways you can import data into the designer: 
 
 * **Azure Machine Learning datasets** - Register [datasets](concept-data.md) in Azure Machine Learning to enable advanced features that help you manage your data.
diff --git a/articles/machine-learning/v1/how-to-designer-python.md b/articles/machine-learning/v1/how-to-designer-python.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, designer, devx-track-python
 
 # Run Python code in Azure Machine Learning designer
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you'll learn how to use the [Execute Python Script](../algorithm-module-reference/execute-python-script.md) component to add custom logic to the Azure Machine Learning designer. In this how-to, you use the Pandas library to do simple feature engineering.
 
 You can use the in-built code editor to quickly add simple Python logic. You should use the zip file method to add more complex code, or to upload additional Python libraries.
diff --git a/articles/machine-learning/v1/how-to-designer-transform-data.md b/articles/machine-learning/v1/how-to-designer-transform-data.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, designer
 
 # Transform data in Azure Machine Learning designer
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to transform and save datasets in the Azure Machine Learning designer, to prepare your own data for machine learning.
 
 You'll use the sample [Adult Census Income Binary Classification](samples-designer.md) dataset to prepare two datasets: one dataset that includes adult census information from only the United States, and another dataset that includes census information from non-US adults.
diff --git a/articles/machine-learning/v1/how-to-enable-app-insights.md b/articles/machine-learning/v1/how-to-enable-app-insights.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, devx-track-python, data4ml, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to collect data from models deployed to web service endpoints in Azure Kubernetes Service (AKS) or Azure Container Instances (ACI). Use [Azure Application Insights](/azure/azure-monitor/app/app-insights-overview) to collect the following data from an endpoint:
 * Output data
 * Responses
diff --git a/articles/machine-learning/v1/how-to-enable-data-collection.md b/articles/machine-learning/v1/how-to-enable-data-collection.md
@@ -16,6 +16,8 @@ ms.custom: UpdateFrequency5, data4ml, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article shows how to collect data from an Azure Machine Learning model deployed on an Azure Kubernetes Service (AKS) cluster. The collected data is then stored in Azure Blob storage.
 
 Once collection is enabled, the data you collect helps you:
diff --git a/articles/machine-learning/v1/how-to-extend-prebuilt-docker-image-inference.md b/articles/machine-learning/v1/how-to-extend-prebuilt-docker-image-inference.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, deploy, docker, prebuilt
 
 # Extend a prebuilt Docker image
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In some cases, the [prebuilt Docker images for model inference](../concept-prebuilt-docker-images-inference.md) and [extensibility](./how-to-prebuilt-docker-images-inference-python-extensibility.md) solutions for Azure Machine Learning may not meet your inference service needs.
 
 In this case, you can use a Dockerfile to create a new image, using one of the prebuilt images as the starting point. By extending from an existing prebuilt Docker image, you can use the Azure Machine Learning network stack and libraries without creating an image from scratch.
diff --git a/articles/machine-learning/v1/how-to-generate-automl-training-code.md b/articles/machine-learning/v1/how-to-generate-automl-training-code.md
@@ -16,6 +16,8 @@ monikerRange: 'azureml-api-1'
 
 #  View training code for an Automated ML model
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to view the generated training code from any automated machine learning trained model. 
 
 Code generation for automated ML trained models allows you to see the following details that automated ML uses to train and build the model for a specific run.
diff --git a/articles/machine-learning/v1/how-to-high-availability-machine-learning.md b/articles/machine-learning/v1/how-to-high-availability-machine-learning.md
@@ -16,6 +16,8 @@ monikerRange: 'azureml-api-1'
 
 # Failover for business continuity and disaster recovery
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 To maximize your uptime, plan ahead to maintain business continuity and prepare for disaster recovery with Azure Machine Learning. 
 
 Microsoft strives to ensure that Azure services are always available. However, unplanned service outages may occur. We recommend having a disaster recovery plan in place for handling regional service outages. In this article, you'll learn how to:
diff --git a/articles/machine-learning/v1/how-to-identity-based-data-access.md b/articles/machine-learning/v1/how-to-identity-based-data-access.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, devx-track-python, data4ml
 
 # Connect to storage by using identity-based data access with SDK v1
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you'll learn how to connect to storage services on Azure with identity-based data access and Azure Machine Learning datastores, via the [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml/intro).  
 
 Typically, datastores use **credential-based authentication** to verify that you have permission to access the storage service. Datastores keep connection information, like your subscription ID and token authorization, in the [key vault](https://azure.microsoft.com/services/key-vault/) associated with the workspace. When you create a datastore that uses **identity-based data access**, your Azure account ([Microsoft Entra token](/azure/active-directory/fundamentals/active-directory-whatis)) is used to confirm that you have permission to access the storage service. In the **identity-based data access** scenario, no authentication credentials are saved. Only the storage account information is stored in the datastore.
diff --git a/articles/machine-learning/v1/how-to-inference-onnx-automl-image-models.md b/articles/machine-learning/v1/how-to-inference-onnx-automl-image-models.md
@@ -20,7 +20,7 @@ ms.custom: UpdateFrequency5, sdkv1
 
 [!INCLUDE [cli-version-info](../includes/machine-learning-cli-v1-deprecation.md)] 
 
-  
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 In this article, you learn how to use Open Neural Network Exchange (ONNX) to make predictions on computer vision models generated from automated machine learning (AutoML) in Azure Machine Learning. 
 
diff --git a/articles/machine-learning/v1/how-to-link-synapse-ml-workspaces.md b/articles/machine-learning/v1/how-to-link-synapse-ml-workspaces.md
@@ -18,6 +18,8 @@ ms.custom: UpdateFrequency5, devx-track-python, data4ml, synapse-azureml, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 > [!WARNING]
 > The Azure Synapse Analytics integration with Azure Machine Learning, available in Python SDK v1, is deprecated. Users can still use Synapse workspace, registered with Azure Machine Learning, as a linked service. However, a new Synapse workspace can no longer be registered with Azure Machine Learning as a linked service. We recommend use of serverless Spark compute and attached Synapse Spark pools, available in CLI v2 and Python SDK v2. For more information, visit [https://aka.ms/aml-spark](https://aka.ms/aml-spark).
 
