adding deprecation note

Blackmist · Blackmist · commit 278f44c384aa · 2025-01-08T09:13:14.000-05:00
diff --git a/articles/machine-learning/v1/how-to-use-managed-identities.md b/articles/machine-learning/v1/how-to-use-managed-identities.md
@@ -18,6 +18,9 @@ ms.custom: UpdateFrequency5, cliv1, sdkv1, devx-track-azurecli
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 [!INCLUDE [cli v1](../includes/machine-learning-cli-v1.md)]
 
+[!INCLUDE [cli-version-info](../includes/machine-learning-cli-v1-deprecation.md)]
+
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 [Managed identities](/azure/active-directory/managed-identities-azure-resources/overview) allow you to configure your workspace with the *minimum required permissions to access resources*. 
 
@@ -35,9 +38,6 @@ In this article, you'll learn how to use managed identities to:
 
 - An Azure Machine Learning workspace. For more information, see [Create workspace resources](../quickstart-create-resources.md).
 - The [Azure CLI extension for Machine Learning service](reference-azure-machine-learning-cli.md)
-
-    [!INCLUDE [cli v1 deprecation](../includes/machine-learning-cli-v1-deprecation.md)]
-
 - The [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro).
 - To assign roles, the login for your Azure subscription must have the [Managed Identity Operator](/azure/role-based-access-control/built-in-roles#managed-identity-operator) role, or other role that grants the required actions (such as __Owner__).
 - You must be familiar with creating and working with [Managed Identities](/azure/active-directory/managed-identities-azure-resources/overview).
diff --git a/articles/machine-learning/v1/how-to-use-mlflow.md b/articles/machine-learning/v1/how-to-use-mlflow.md
@@ -16,6 +16,8 @@ ms.custom: UpdateFrequency5, mlflow, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, learn how to enable [MLflow Tracking](https://mlflow.org/docs/latest/quickstart.html#using-the-tracking-api) to connect Azure Machine Learning as the backend of your MLflow experiments.
 
 [MLflow](https://www.mlflow.org) is an open-source library for managing the lifecycle of your machine learning experiments. MLflow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment--locally on your computer, on a remote compute target, a virtual machine, or an [Azure Databricks cluster](../how-to-use-mlflow-azure-databricks.md).
diff --git a/articles/machine-learning/v1/how-to-use-pipeline-parameter.md b/articles/machine-learning/v1/how-to-use-pipeline-parameter.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, designer
 
 # Use pipeline parameters in the designer to build versatile pipelines
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Use pipeline parameters to build flexible pipelines in the designer. Pipeline parameters let you dynamically set values at runtime to encapsulate pipeline logic and reuse assets.
 
 Pipeline parameters are especially useful when resubmitting a pipeline job, [retraining models](how-to-retrain-designer.md), or [performing batch predictions](how-to-run-batch-predictions-designer.md).
diff --git a/articles/machine-learning/v1/how-to-use-private-python-packages.md b/articles/machine-learning/v1/how-to-use-private-python-packages.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)]
+
 In this article, learn how to use private Python packages securely within Azure Machine Learning. Use cases for private Python packages include:
 
  * You've developed a private package that you don't want to share publicly.
diff --git a/articles/machine-learning/v1/how-to-use-secrets-in-runs.md b/articles/machine-learning/v1/how-to-use-secrets-in-runs.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, sdkv1, FY25Q1-Linter
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you learn how to use secrets in training jobs securely. Authentication information such as your user name and password are secrets. For example, if you connect to an external database in order to query training data, you would need to pass your username and password to the remote job context. Coding such values into training scripts in cleartext is insecure as it would expose the secret. 
 
 Instead, your Azure Machine Learning workspace has an associated resource called a [Azure Key Vault](/azure/key-vault/general/overview). Use this Key Vault to pass secrets to remote jobs securely through a set of APIs in the Azure Machine Learning Python SDK.
diff --git a/articles/machine-learning/v1/how-to-use-synapsesparkstep.md b/articles/machine-learning/v1/how-to-use-synapsesparkstep.md
@@ -18,6 +18,8 @@ ms.custom: UpdateFrequency5, 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-version-track-datasets.md b/articles/machine-learning/v1/how-to-version-track-datasets.md
@@ -18,6 +18,8 @@ ms.custom: UpdateFrequency5, data4ml, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, you'll learn how to version and track Azure Machine Learning datasets for reproducibility. Dataset versioning bookmarks specific states of your data, so that you can apply a specific version of the dataset for future experiments.
 
 You might want to version your Azure Machine Learning resources in these typical scenarios:
diff --git a/articles/machine-learning/v1/introduction.md b/articles/machine-learning/v1/introduction.md
@@ -18,6 +18,10 @@ ms.custom: UpdateFrequency5, cliv1
 
 [!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)]
+
 
 All articles in this section document the use of the first version of Azure Machine Learning Python SDK (v1) or Azure CLI ml extension (v1). For information on the current SDK and CLI, see [Azure Machine Learning SDK and CLI v2](../concept-v2.md).
 
diff --git a/articles/machine-learning/v1/reference-automl-images-hyperparameters.md b/articles/machine-learning/v1/reference-automl-images-hyperparameters.md
@@ -16,6 +16,8 @@ ms.date: 01/18/2022
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Learn which hyperparameters are available specifically for computer vision tasks in automated ML experiments.
 
 With support for computer vision tasks, you can control the model algorithm and sweep hyperparameters. These model algorithms and hyperparameters are passed in as the parameter space for the sweep. While many of the hyperparameters exposed are model-agnostic, there are instances where hyperparameters are model-specific or task-specific.
diff --git a/articles/machine-learning/v1/reference-automl-images-schema.md b/articles/machine-learning/v1/reference-automl-images-schema.md
@@ -19,6 +19,8 @@ ms.date: 10/13/2021
     
 [!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/samples-designer.md b/articles/machine-learning/v1/samples-designer.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, designer
 
 # Example pipelines & datasets for Azure Machine Learning designer
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Use the built-in examples in Azure Machine Learning designer to quickly get started building your own machine learning pipelines. The Azure Machine Learning designer [GitHub repository](https://github.com/Azure/MachineLearningDesigner) contains detailed documentation to help you understand some common  machine learning scenarios.
 
 ## Prerequisites
diff --git a/articles/machine-learning/v1/samples-notebooks.md b/articles/machine-learning/v1/samples-notebooks.md
@@ -19,6 +19,8 @@ ms.custom: UpdateFrequency5
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 The [Azure Machine Learning Notebooks repository](https://github.com/azure/machinelearningnotebooks) includes Azure Machine Learning Python SDK (v1) samples. These Jupyter notebooks are designed to help you explore the SDK and serve as models for your own machine learning projects.  In this repository, you'll find tutorial notebooks in the **tutorials** folder and feature-specific notebooks in the **how-to-use-azureml** folder.
 
 This article shows you how to access the repositories from the following environments:
diff --git a/articles/machine-learning/v1/tutorial-1st-experiment-hello-world.md b/articles/machine-learning/v1/tutorial-1st-experiment-hello-world.md
@@ -17,6 +17,7 @@ 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 run your first Python script in the cloud with Azure Machine Learning. This tutorial is *part 1 of a two-part tutorial series*.
 
diff --git a/articles/machine-learning/v1/tutorial-1st-experiment-sdk-train.md b/articles/machine-learning/v1/tutorial-1st-experiment-sdk-train.md
@@ -17,6 +17,8 @@ ms.custom: UpdateFrequency5, devx-track-python, contperf-fy21q, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 
 This tutorial shows you how to train a machine learning model in Azure Machine Learning. This tutorial is *part 2 of a two-part tutorial series*.
 
diff --git a/articles/machine-learning/v1/tutorial-auto-train-image-models.md b/articles/machine-learning/v1/tutorial-auto-train-image-models.md
@@ -10,13 +10,14 @@ author: swatig007
 ms.author: swatig
 ms.reviewer: nibaccam
 ms.date: 10/06/2021
-ms.custom: UpdateFrequency5, devx-track-python, automl, sdkv2
+ms.custom: UpdateFrequency5, devx-track-python, automl, sdkv1
 ---
 
 # Tutorial: Train an object detection model (preview) with AutoML and Python (v1)
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
-    
+
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 >[!IMPORTANT]
 > The features presented in this article are in preview. They should be considered [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview features that might change at any time.
diff --git a/articles/machine-learning/v1/tutorial-designer-automobile-price-deploy.md b/articles/machine-learning/v1/tutorial-designer-automobile-price-deploy.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, designer
 
 # Tutorial: Use the designer to deploy a machine learning model
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In [part one of this tutorial](tutorial-designer-automobile-price-train-score.md), you trained a linear regression model that predicts car prices. In this second part, you use the Azure Machine Learning designer to deploy the model so that others can use it.
 
 >[!Note]
diff --git a/articles/machine-learning/v1/tutorial-designer-automobile-price-train-score.md b/articles/machine-learning/v1/tutorial-designer-automobile-price-train-score.md
@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, designer, build-2023
 
 # Tutorial: Designer - train a no-code regression model
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Train a linear regression model that predicts car prices using the Azure Machine Learning designer. This tutorial is part one of a two-part series.
 
 This tutorial uses the Azure Machine Learning designer, for more information, see [What is Azure Machine Learning designer?](concept-designer.md)
diff --git a/articles/machine-learning/v1/tutorial-pipeline-python-sdk.md b/articles/machine-learning/v1/tutorial-pipeline-python-sdk.md
@@ -16,6 +16,8 @@ ms.custom: UpdateFrequency5, devx-track-python, sdkv1
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 > [!NOTE]
 > For a tutorial that uses SDK v2 to build a pipeline, see [Tutorial: Use ML pipelines for production ML workflows with Python SDK v2 in a Jupyter Notebook](../tutorial-pipeline-python-sdk.md).
 
diff --git a/articles/machine-learning/v1/tutorial-train-deploy-notebook.md b/articles/machine-learning/v1/tutorial-train-deploy-notebook.md
@@ -18,6 +18,8 @@ ms.custom: UpdateFrequency5, sdkv1, devx-track-python
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this tutorial, you train a machine learning model on remote compute resources. You use the training and deployment workflow for Azure Machine Learning in a Python Jupyter Notebook. You can then use the notebook as a template to train your own machine learning model with your own data. 
 
 This tutorial trains a simple logistic regression by using the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset and [scikit-learn](https://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28 x 28 pixels, representing a number from zero to nine. The goal is to create a multi-class classifier to identify the digit a given image represents.
