adding deprecation note

Author: Blackmist

articles/machine-learning/v1/algorithm-cheat-sheet.md

@@ -14,6 +14,8 @@ adobe-target: true
 ---
 # Machine Learning Algorithm Cheat Sheet for Azure Machine Learning designer
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 The **Azure Machine Learning Algorithm Cheat Sheet** helps you choose the right algorithm from the designer for a predictive analytics model.
 
 >[!Note]

articles/machine-learning/v1/concept-automated-ml.md

@@ -16,6 +16,8 @@ ms.custom: UpdateFrequency5, automl
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Automated ML in Azure Machine Learning is based on a breakthrough from our [Microsoft Research division](https://www.microsoft.com/research/project/automl/).
 
 Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. With automated machine learning, you'll accelerate the time it takes to get production-ready ML models with great ease and efficiency.

articles/machine-learning/v1/concept-azure-machine-learning-architecture.md

@@ -19,6 +19,8 @@ monikerRange: 'azureml-api-1'
 
 [!INCLUDE [dev v1](../includes/machine-learning-dev-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 This article applies to the first version (v1) of the Azure Machine Learning CLI & SDK. For version two (v2), see  [How Azure Machine Learning works (v2)](../concept-azure-machine-learning-v2.md).
 
 Learn about the architecture and concepts for [Azure Machine Learning](../overview-what-is-azure-machine-learning.md).  This article gives you a high-level understanding of the components and how they work together to assist in the process of building, deploying, and maintaining machine learning models.  

articles/machine-learning/v1/concept-data.md

@@ -19,6 +19,8 @@ ms.custom: UpdateFrequency5, data4ml
 [!INCLUDE [CLI v1](../includes/machine-learning-cli-v1.md)]
 [!INCLUDE [SDK v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Azure Machine Learning makes it easy to connect to your data in the cloud. It provides an abstraction layer over the underlying storage service, so that you can securely access and work with your data without the need to write code specific to your storage type. Azure Machine Learning also provides these data capabilities:
 
 *    Interoperability with Pandas and Spark DataFrames

articles/machine-learning/v1/concept-designer.md

@@ -15,6 +15,8 @@ ms.custom: UpdateFrequency5, designer, training
 
 # What is Designer (v1) in Azure Machine Learning?
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 The Azure Machine Learning designer is a drag-and-drop interface used to train and deploy models in Azure Machine Learning studio. This article describes the tasks you can do in the designer.
 
 > [!IMPORTANT]

articles/machine-learning/v1/concept-model-management-and-deployment.md

@@ -18,6 +18,8 @@ ms.date: 08/12/2024
 
 [!INCLUDE [dev v1](../includes/machine-learning-dev-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 In this article, learn how to apply machine learning operations (MLOps) practices in Azure Machine Learning to manage the lifecycle of your models. Machine learning operations practices can improve the quality and consistency of your machine learning solutions.
 
 > [!IMPORTANT]

articles/machine-learning/v1/concept-network-data-access.md

@@ -19,6 +19,8 @@ ms.date: 11/16/2022
 [!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)]
+
 Data access is complex and it's important to recognize that there are many pieces to it. For example, accessing data from Azure Machine Learning studio is different than using the SDK. When using the SDK on your local development environment, you're directly accessing data in the cloud. When using studio, you aren't always directly accessing the data store from your client. Studio relies on the workspace to access data on your behalf.
 
 > [!IMPORTANT]

articles/machine-learning/v1/concept-train-machine-learning-model.md

@@ -18,6 +18,8 @@ ms.devlang: azurecli
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
+[!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
+
 Azure Machine Learning provides several ways to train your models, from code-first solutions using the SDK to low-code solutions such as automated machine learning and the visual designer. Use the following list to determine which training method is right for you:
 
 + [Azure Machine Learning SDK for Python](#python-sdk): The Python SDK provides several ways to train models, each with different capabilities.

articles/machine-learning/v1/how-to-create-machine-learning-pipelines.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)]
+
 In this article, you learn how to create and run [machine learning pipelines](../concept-ml-pipelines.md) by using the [Azure Machine Learning SDK](/python/api/overview/azure/ml/intro). Use **ML pipelines** to create a workflow that stitches together various ML phases. Then, publish that pipeline for later access or sharing with others. Track ML pipelines to see how your model is performing in the real world and to detect data drift. ML pipelines are ideal for batch scoring scenarios, using various computes, reusing steps instead of rerunning them, and sharing ML workflows with others.
 
 This article isn't a tutorial. For guidance on creating your first pipeline, see [Tutorial: Build an Azure Machine Learning pipeline for batch scoring](../tutorial-pipeline-batch-scoring-classification.md) or [Use automated ML in an Azure Machine Learning pipeline in Python](how-to-use-automlstep-in-pipelines.md).