adding missing deprecation note

Author: Blackmist

articles/machine-learning/concept-ml-pipelines.md

@@ -19,6 +19,8 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 [!INCLUDE [dev v1](includes/machine-learning-dev-v1.md)]
 
 [!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [cli v1 deprecation](./includes/machine-learning-cli-v1-deprecation.md)]
 :::moniker-end
 :::moniker range="azureml-api-2"
 [!INCLUDE [dev v2](includes/machine-learning-dev-v2.md)]

articles/machine-learning/how-to-network-security-overview.md

@@ -25,6 +25,8 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 [!INCLUDE [dev v1](includes/machine-learning-dev-v1.md)]
 
 [!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [cli v1 deprecation](./includes/machine-learning-cli-v1-deprecation.md)]
 :::moniker-end
 
 [!INCLUDE [managed-vnet-note](includes/managed-vnet-note.md)]

articles/machine-learning/how-to-troubleshoot-environments.md

@@ -23,6 +23,8 @@ monikerRange: 'azureml-api-1 || azureml-api-2'
 [!INCLUDE [dev v1](includes/machine-learning-dev-v1.md)]
 
 [!INCLUDE [v1 deprecation](includes/sdk-v1-deprecation.md)]
+
+[!INCLUDE [cli v1 deprecation](./includes/machine-learning-cli-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.

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

@@ -21,6 +21,8 @@ monikerRange: 'azureml-api-1'
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
+[!INCLUDE [cli v1 deprecation](../includes/machine-learning-cli-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-model-management-and-deployment.md

@@ -20,6 +20,8 @@ ms.date: 08/12/2024
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
+[!INCLUDE [cli v1 deprecation](../includes/machine-learning-cli-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/how-to-configure-private-link.md

@@ -19,6 +19,8 @@ ms.date: 07/26/2024
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
+[!INCLUDE [cli v1 deprecation](../includes/machine-learning-cli-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).
 
 Azure Private Link enables you to connect to your workspace using a private endpoint. The private endpoint is a set of private IP addresses within your virtual network. You can then limit access to your workspace to only occur over the private IP addresses. A private endpoint helps reduce the risk of data exfiltration. To learn more about private endpoints, see the [Azure Private Link](/azure/private-link/private-link-overview) article.