adding deprecation note

Blackmist · Blackmist · commit 74d90f8290e3 · 2025-01-08T13:20:44.000-05:00
diff --git a/articles/machine-learning/v1/how-to-access-data.md b/articles/machine-learning/v1/how-to-access-data.md
@@ -19,6 +19,8 @@ ms.custom: UpdateFrequency5, data4ml
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 [!INCLUDE [cli v1](../includes/machine-learning-cli-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, learn how to connect to data storage services on Azure with Azure Machine Learning datastores and the [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro).
 
 Datastores securely connect to your storage service on Azure, and they avoid risk to your authentication credentials or the integrity of your original data store. A datastore stores connection information - for example, your subscription ID or token authorization - in the [Key Vault](https://azure.microsoft.com/services/key-vault/) associated with the workspace. With a datastore, you can securely access your storage because you can avoid hard-coding connection information in your scripts. You can create datastores that connect to [these Azure storage solutions](#supported-data-storage-service-types).
diff --git a/articles/machine-learning/v1/how-to-authenticate-web-service.md b/articles/machine-learning/v1/how-to-authenticate-web-service.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Azure Machine Learning allows you to deploy your trained machine learning models as web services. In this article, learn how to configure authentication for these deployments.
 
 The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods:
diff --git a/articles/machine-learning/v1/how-to-auto-train-forecast.md b/articles/machine-learning/v1/how-to-auto-train-forecast.md
@@ -18,6 +18,7 @@ show_latex: true
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 In this article, you learn how to set up AutoML training for time-series forecasting models with Azure Machine Learning automated ML in the [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/).
 
diff --git a/articles/machine-learning/v1/how-to-auto-train-image-models.md b/articles/machine-learning/v1/how-to-auto-train-image-models.md
@@ -20,6 +20,8 @@ ms.custom: UpdateFrequency5, sdkv1, devx-track-python
     
 [!INCLUDE [cli-version-info](../includes/machine-learning-cli-v1-deprecation.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 
 > [!IMPORTANT]
 > This feature is currently in public preview. This preview version is provided without a service-level agreement. Certain features might not be supported or might have constrained capabilities. For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
diff --git a/articles/machine-learning/v1/how-to-auto-train-models-v1.md b/articles/machine-learning/v1/how-to-auto-train-models-v1.md
@@ -19,6 +19,8 @@ ms.custom: UpdateFrequency5, devx-track-python, automl, 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 train a regression model with the Azure Machine Learning Python SDK by using Azure Machine Learning Automated ML. The regression model predicts passenger fares for taxi cabs operating in New York City (NYC). You write code with the Python SDK to configure a workspace with prepared data, train the model locally with custom parameters, and explore the results.
 
 The process accepts training data and configuration settings. It automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model. The following diagram illustrates the process flow for the regression model training:
diff --git a/articles/machine-learning/v1/how-to-auto-train-nlp-models.md b/articles/machine-learning/v1/how-to-auto-train-nlp-models.md
@@ -17,6 +17,8 @@ ms.date: 03/15/2022
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 [!INCLUDE [preview disclaimer](../includes/machine-learning-preview-generic-disclaimer.md)]
 
 In this article, you learn how to train natural language processing (NLP) models with [automated ML](../concept-automated-ml.md) in the [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/).
diff --git a/articles/machine-learning/v1/how-to-configure-auto-features.md b/articles/machine-learning/v1/how-to-configure-auto-features.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)]
+
 This article explains how to customize the data featurization settings in Azure Machine Learning for your [automated machine learning (AutoML) experiments](../concept-automated-ml.md).
 
 ## Feature engineering and featurization
diff --git a/articles/machine-learning/v1/how-to-configure-auto-train.md b/articles/machine-learning/v1/how-to-configure-auto-train.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, devx-track-python, automl, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this guide, learn how to set up an automated machine learning, AutoML, training run with the [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro) using Azure Machine Learning automated ML. Automated ML picks an algorithm and hyperparameters for you and generates a model ready for deployment. This guide provides details of the various options that you can use to configure automated ML experiments.
 
 For an end to end example, see [Tutorial: AutoML- train regression model](how-to-auto-train-models-v1.md).
diff --git a/articles/machine-learning/v1/how-to-configure-cross-validation-data-splits.md b/articles/machine-learning/v1/how-to-configure-cross-validation-data-splits.md
@@ -20,6 +20,8 @@ monikerRange: 'azureml-api-1'
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article describes options for configuring training data and validation data splits along with cross-validation settings for your automated machine learning (automated ML) experiments. In Azure Machine Learning, when you use automated ML to build multiple machine learning models, each child run needs to validate the related model by calculating the quality metrics for that model, such as accuracy or area under the curve (AUC) weighted. These metrics are calculated by comparing the predictions made with each model with real labels from past observations in the validation data. Automated ML experiments perform model validation automatically.
 
 The following sections describe how you can customize validation settings with the [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/). To learn more about how metrics are calculated based on validation type, see the [Set metric calculation for cross validation](#set-metric-calculation-for-cross-validation) section. If you're interesting in a low-code or no-code experience, see [Create your automated ML experiments in Azure Machine Learning studio](../how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment). 
diff --git a/articles/machine-learning/v1/how-to-configure-databricks-automl-environment.md b/articles/machine-learning/v1/how-to-configure-databricks-automl-environment.md
@@ -16,6 +16,8 @@ monikerRange: 'azureml-api-1'
 
 # Set up a development environment with Azure Databricks and AutoML in Azure Machine Learning 
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Learn how to configure a development environment in Azure Machine Learning that uses Azure Databricks and automated ML.
 
 Azure Databricks is ideal for running large-scale intensive machine learning workflows on the scalable Apache Spark platform in the Azure cloud. It provides a collaborative Notebook-based environment with a CPU or GPU-based compute cluster.
diff --git a/articles/machine-learning/v1/how-to-configure-environment.md b/articles/machine-learning/v1/how-to-configure-environment.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, devx-track-python, devx-track-azurecli, sdkv1, buil
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Learn how to configure a Python development environment for Azure Machine Learning.
 
 The following table shows each development environment covered in this article, along with pros and cons.
diff --git a/articles/machine-learning/v1/how-to-configure-private-link.md b/articles/machine-learning/v1/how-to-configure-private-link.md
@@ -17,6 +17,7 @@ ms.date: 07/26/2024
 
 [!INCLUDE [dev v1](../includes/machine-learning-dev-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 In this document, you learn how to configure a private endpoint for your Azure Machine Learning workspace. For information on creating a virtual network for Azure Machine Learning, see [Virtual network isolation and privacy overview](../how-to-network-security-overview.md).
 
diff --git a/articles/machine-learning/v1/how-to-connect-data-ui.md b/articles/machine-learning/v1/how-to-connect-data-ui.md
@@ -16,6 +16,8 @@ ms.custom: UpdateFrequency5, data4ml
 
 # Connect to data with the Azure Machine Learning studio
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article shows how to access your data with the [Azure Machine Learning studio](https://ml.azure.com). Connect to your data in Azure storage services with [Azure Machine Learning datastores](how-to-access-data.md). Then, package that data for ML workflow tasks with [Azure Machine Learning datasets](how-to-create-register-datasets.md).
 
 This table defines and summarizes the benefits of datastores and datasets.
diff --git a/articles/machine-learning/v1/how-to-consume-web-service.md b/articles/machine-learning/v1/how-to-consume-web-service.md
@@ -18,6 +18,7 @@ ms.custom: UpdateFrequency5, devx-track-python, devx-track-csharp, cliv1, sdkv1,
 
 # Consume an Azure Machine Learning model deployed as a web service
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model. In this document, learn how to create clients for the web service by using C#, Go, Java, and Python.
 
diff --git a/articles/machine-learning/v1/how-to-convert-ml-experiment-to-production.md b/articles/machine-learning/v1/how-to-convert-ml-experiment-to-production.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, devx-track-python, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this tutorial, you learn how to convert Jupyter notebooks into Python scripts to make it testing and automation friendly using the MLOpsPython code template and Azure Machine Learning. Typically, this process is used to take experimentation / training code from a Jupyter notebook and convert it into Python scripts. Those scripts can then be used testing and CI/CD automation in your production environment. 
 
 A machine learning project requires experimentation where hypotheses are tested with agile tools like Jupyter Notebook using real datasets. Once the model is ready for production, the model code should be placed in a production code repository. In some cases, the model code must be converted to Python scripts to be placed in the production code repository. This tutorial covers a recommended approach on how to export experimentation code to Python scripts.
diff --git a/articles/machine-learning/v1/how-to-create-attach-compute-cluster.md b/articles/machine-learning/v1/how-to-create-attach-compute-cluster.md
@@ -17,6 +17,7 @@ ms.date: 05/02/2022
 
 [!INCLUDE [dev v1](../includes/machine-learning-dev-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 Learn how to create and manage a [compute cluster](../concept-compute-target.md#azure-machine-learning-compute-managed) in your Azure Machine Learning workspace.
 
diff --git a/articles/machine-learning/v1/how-to-create-attach-kubernetes.md b/articles/machine-learning/v1/how-to-create-attach-kubernetes.md
@@ -17,6 +17,8 @@ ms.date: 04/21/2022
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 [!INCLUDE [cli v1](../includes/machine-learning-cli-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 
 > [!IMPORTANT]
 > This article shows how to use the CLI and SDK v1 to create or attach an Azure Kubernetes Service cluster, which is considered as **legacy** feature now.  To attach Azure Kubernetes Service cluster using  the recommended approach for v2, see [Introduction to Kubernetes compute target in v2](../how-to-attach-kubernetes-anywhere.md).
diff --git a/articles/machine-learning/v1/how-to-create-manage-compute-instance.md b/articles/machine-learning/v1/how-to-create-manage-compute-instance.md
@@ -17,6 +17,7 @@ ms.date: 05/02/2022
 
 [!INCLUDE [dev v1](../includes/machine-learning-dev-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 Learn how to create and manage a [compute instance](../concept-compute-instance.md) in your Azure Machine Learning workspace with CLI v1.
 
diff --git a/articles/machine-learning/v1/how-to-create-register-datasets.md b/articles/machine-learning/v1/how-to-create-register-datasets.md
@@ -18,6 +18,8 @@ ms.date: 02/28/2024
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to create Azure Machine Learning datasets to access data for your local or remote experiments with the Azure Machine Learning Python SDK. For more information about how datasets fit in Azure Machine Learning's overall data access workflow, visit the [Securely access data](concept-data.md#data-workflow) article.
 
 When you create a dataset, you create a reference to the data source location, along with a copy of its metadata. Because the data remains in its existing location, you incur no extra storage cost, and you don't risk the integrity of your data sources. Additionally, datasets are lazily evaluated, which helps improve workflow performance speeds. You can create datasets from datastores, public URLs, and [Azure Open Datasets](../../open-datasets/how-to-create-azure-machine-learning-dataset-from-open-dataset.md). For information about a low-code experience, visit [Create Azure Machine Learning datasets with the Azure Machine Learning studio](how-to-connect-data-ui.md#create-data-assets).
diff --git a/articles/machine-learning/v1/how-to-train-distributed-gpu.md b/articles/machine-learning/v1/how-to-train-distributed-gpu.md
@@ -16,6 +16,8 @@ ms.custom: UpdateFrequency5, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Learn more about how to use distributed GPU training code in Azure Machine Learning (ML). This article will not teach you about distributed training.  It will help you run your existing distributed training code on Azure Machine Learning. It offers tips and examples for you to follow for each framework:
 
 * Message Passing Interface (MPI)

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