Merge pull request #229103 from Blackmist/preview-disclaimer

Preview disclaimer

Author: prmerger-automator[bot]

articles/machine-learning/apache-spark-azure-ml-concepts.md

@@ -21,6 +21,8 @@ Azure Machine Learning integration with Azure Synapse Analytics (preview) provid
 - Managed (Automatic) Spark compute
 - Attached Synapse Spark pool
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Managed (Automatic) Spark compute
 
 With the Apache Spark framework, Azure Machine Learning Managed (Automatic) Spark compute is the easiest way to accomplish distributed computing tasks in the Azure Machine Learning environment. Azure Machine Learning offers a fully managed, serverless, on-demand Apache Spark compute cluster. Its users can avoid the need to create an Azure Synapse workspace and a Synapse Spark pool.

articles/machine-learning/concept-designer.md

@@ -103,8 +103,6 @@ Online endpoints must be deployed to an Azure Kubernetes Service cluster.
 
 To learn how to deploy your model, see [Tutorial: Deploy a machine learning model with the designer](tutorial-designer-automobile-price-deploy.md).
 
-[!INCLUDE [endpoints-option](../../includes/machine-learning-endpoints-preview-note.md)]
-
 ## Publish
 
 You can also publish a pipeline to a **pipeline endpoint**. Similar to an online endpoint, a pipeline endpoint lets you submit new pipeline jobs from external applications using REST calls. However, you cannot send or receive data in real time using a pipeline endpoint.

articles/machine-learning/concept-endpoints.md

@@ -21,6 +21,11 @@ ms.date: 02/07/2023
 
 Use Azure Machine Learning endpoints to streamline model deployments for both real-time and batch inference deployments. Endpoints provide a unified interface to invoke and manage model deployments across compute types.
 
+> [!IMPORTANT]
+> Items marked (preview) in this article are currently in public preview.
+> The preview version is provided without a service level agreement, and it's not recommended for production workloads. 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/).
+
 In this article, you learn about:
 > [!div class="checklist"]
 > * Endpoints

articles/machine-learning/concept-enterprise-security.md

@@ -23,6 +23,11 @@ In this article, you'll learn about security and governance features available f
 * Scan for vulnerabilities
 * Apply and audit configuration policies
 
+> [!IMPORTANT]
+> Items marked (preview) in this article are currently in public preview.
+> The preview version is provided without a service level agreement, and it's not recommended for production workloads. 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/).
+
 ## Restrict access to resources and operations
 
 [Azure Active Directory (Azure AD)](../active-directory/fundamentals/active-directory-whatis.md) is the identity service provider for Azure Machine Learning. It allows you to create and manage the security objects (user, group, service principal, and managed identity) that are used to _authenticate_ to Azure resources. Multi-factor authentication is supported if Azure AD is configured to use it.

articles/machine-learning/concept-machine-learning-registries-mlops.md

@@ -25,6 +25,8 @@ In such scenarios, you may be using different Azure Machine Learning workspaces
 * You need to train a model in a development workspace but deploy it an endpoint in a production workspace, possibly in a different Azure subscription or region. In this case, you must be able to trace back the training job. For example, to analyze the metrics, logs, code, environment, and data used to train the model if you encounter accuracy or performance issues with the production deployment. 
 * You need to develop a training pipeline with test data or anonymized data in the development workspace but retrain the model with production data in the production workspace. In this case, you may need to compare training metrics on sample vs production data to ensure the training optimizations are performing well with actual data. 
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Cross-workspace MLOps with registries
 
 Registries, much like a Git repository, decouples ML assets from workspaces and hosts them in a central location, making them available to all workspaces in your organization.

articles/machine-learning/concept-mlflow.md

@@ -32,6 +32,11 @@ ms.custom: devx-track-python, cliv2, sdkv2, event-tier1-build-2022, ignite-2022
 > [!NOTE]
 > Unlike the Azure Machine Learning SDK v1, there's no logging functionality in the SDK v2 and we recommend using MLflow for logging. Such strategy allows your training routines to become cloud-agnostic and portable, removing any dependency in your code with Azure Machine Learning.
 
+> [!IMPORTANT]
+> Items marked (preview) in this article are currently in public preview.
+> The preview version is provided without a service level agreement, and it's not recommended for production workloads. 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/).
+
 ## Tracking with MLflow
 
 Azure Machine Learning uses MLflow Tracking for metric logging and artifact storage for your experiments. When connected to Azure Machine Learning, all tracking performed using MLflow is materialized in the workspace you are working on. To learn more about how to instrument your experiments for tracking experiments and training routines, see [Log metrics, parameters, and files with MLflow](how-to-log-view-metrics.md). You can also use MLflow to [Query & compare experiments and runs with MLflow](how-to-track-experiments-mlflow.md).

articles/machine-learning/concept-plan-manage-cost.md

@@ -22,6 +22,11 @@ Understand that the costs for Azure Machine Learning are only a portion of the m
 
 For more information on optimizing costs, see [how to manage and optimize cost in Azure Machine Learning](how-to-manage-optimize-cost.md).
 
+> [!IMPORTANT]
+> Items marked (preview) in this article are currently in public preview.
+> The preview version is provided without a service level agreement, and it's not recommended for production workloads. 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/).
+
 ## Prerequisites
 
 Cost analysis in Cost Management supports most Azure account types, but not all of them. To view the full list of supported account types, see [Understand Cost Management data](../cost-management-billing/costs/understand-cost-mgt-data.md?WT.mc_id=costmanagementcontent_docsacmhorizontal_-inproduct-learn). 

articles/machine-learning/how-to-run-batch-predictions-designer.md

@@ -26,8 +26,6 @@ In this how-to, you learn to do the following tasks:
 
 To learn how to set up batch scoring services using the SDK, see the accompanying [tutorial on pipeline batch scoring](./tutorial-pipeline-batch-scoring-classification.md).
 
-[!INCLUDE [endpoints-option](../../includes/machine-learning-endpoints-preview-note.md)]
-
 ## Prerequisites
 
 This how-to assumes you already have a training pipeline. For a guided introduction to the designer, complete [part one of the designer tutorial](tutorial-designer-automobile-price-train-score.md).