flagging of preview items

Larry Franks · Larry Franks · commit 376242ce1973 · 2023-03-01T15:34:15.000-05:00
diff --git a/articles/machine-learning/concept-responsible-ai-scorecard.md b/articles/machine-learning/concept-responsible-ai-scorecard.md
@@ -23,6 +23,8 @@ Our Responsible AI dashboard is designed for machine learning professionals and
 
 One of the biggest benefits of using the Azure Machine Learning ecosystem is related to the archival of model and data insights in the Azure Machine Learning Run History (for quick reference in future). As a part of that infrastructure and to accompany machine learning models and their corresponding Responsible AI dashboards, we introduce the Responsible AI scorecard to empower ML professionals to generate and share their data and model health records easily.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Who should use a Responsible AI scorecard?
 
 - If you're a data scientist or a machine learning professional, after training your model and generating its corresponding Responsible AI dashboard(s) for assessment and decision-making purposes, you can extract those learnings via our PDF scorecard and share the report easily with your technical and non-technical stakeholders to build trust and gain their approval for deployment.  
diff --git a/articles/machine-learning/how-to-create-manage-compute-instance.md b/articles/machine-learning/how-to-create-manage-compute-instance.md
@@ -25,6 +25,11 @@ Learn how to create and manage a [compute instance](concept-compute-instance.md)
 
 Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#training-compute-targets).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
 
+> [!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 how to:
 
 * [Create](#create) a compute instance
@@ -52,11 +57,6 @@ Compute instances can run jobs securely in a [virtual network environment](how-t
 
 ## Create
 
-> [!IMPORTANT]
-> Items marked (preview) below 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/).
-
 **Time estimate**: Approximately 5 minutes.
 
 Creating a compute instance is a one time process for your workspace. You can reuse the compute as a development workstation or as a compute target for training. You can have multiple compute instances attached to your workspace. 
diff --git a/articles/machine-learning/how-to-customize-compute-instance.md b/articles/machine-learning/how-to-customize-compute-instance.md
@@ -31,6 +31,8 @@ Some examples of what you can do in a setup script:
 * Set environment variables
 * Install JupyterLab extensions
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Create the setup script
 
 The setup script is a shell script, which runs as `rootuser`.  Create or upload the script into your **Notebooks** files:
diff --git a/articles/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code.md b/articles/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code.md
@@ -23,6 +23,8 @@ Learn how to use the Visual Studio Code (VS Code) debugger to test and debug onl
 
 Azure Machine Learning local endpoints help you test and debug your scoring script, environment configuration, code configuration, and machine learning model locally.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Online endpoint local debugging
 
 Debugging endpoints locally before deploying them to the cloud can help you catch errors in your code and configuration earlier. You have different options for debugging endpoints locally with VS Code.
diff --git a/articles/machine-learning/how-to-debug-visual-studio-code.md b/articles/machine-learning/how-to-debug-visual-studio-code.md
@@ -30,6 +30,8 @@ Use the Azure Machine Learning extension to validate, run, and debug your machin
     > [!IMPORTANT]
     > The Azure Machine Learning VS Code extension uses the CLI (v2) by default. The instructions in this guide use 1.0 CLI. To switch to the 1.0 CLI, set the `azureML.CLI Compatibility Mode` setting in Visual Studio Code to `1.0`. For more information on modifying your settings in Visual Studio Code, see the [user and workspace settings documentation](https://code.visualstudio.com/docs/getstarted/settings).
 
+    [!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 * [Docker](https://www.docker.com/get-started)
   * Docker Desktop for Mac and Windows
   * Docker Engine for Linux.
diff --git a/articles/machine-learning/how-to-deploy-with-triton.md b/articles/machine-learning/how-to-deploy-with-triton.md
@@ -28,6 +28,8 @@ In this article, you will learn how to deploy Triton and a model to a [managed o
 > * [NVIDIA Triton Inference Server](https://aka.ms/nvidia-triton-docs) is an open-source third-party software that is integrated in Azure Machine Learning.
 > * While Azure Machine Learning online endpoints are generally available, _using Triton with an online endpoint/deployment is still in preview_. 
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Prerequisites
 
 # [Azure CLI](#tab/azure-cli)
diff --git a/articles/machine-learning/how-to-inference-server-http.md b/articles/machine-learning/how-to-inference-server-http.md
@@ -21,6 +21,8 @@ The server can also be used to create validation gates in a continuous integrati
 
 This article mainly targets users who want to use the inference server to debug locally, but it will also help you understand how to use the inference server with online endpoints.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Online endpoint local debugging
 
 Debugging endpoints locally before deploying them to the cloud can help you catch errors in your code and configuration earlier. To debug endpoints locally, you could use:
diff --git a/articles/machine-learning/how-to-machine-learning-fairness-aml.md b/articles/machine-learning/how-to-machine-learning-fairness-aml.md
@@ -26,6 +26,8 @@ In this how-to guide, you will learn to use the [Fairlearn](https://fairlearn.gi
 >[!NOTE]
 > Fairness assessment is not a purely technical exercise. **This package can help you assess the fairness of a machine learning model, but only you can configure and make decisions as to how the model performs.**  While this package helps to identify quantitative metrics to assess fairness, developers of machine learning models must also perform a qualitative analysis to evaluate the fairness of their own models.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Azure Machine Learning Fairness SDK 
 
 The Azure Machine Learning Fairness SDK, `azureml-contrib-fairness`, integrates the open-source Python package, [Fairlearn](http://fairlearn.github.io),
diff --git a/articles/machine-learning/how-to-machine-learning-interpretability-aml.md b/articles/machine-learning/how-to-machine-learning-interpretability-aml.md
@@ -32,6 +32,7 @@ In this how-to guide, you learn to use the interpretability package of the Azure
 
 * Deploy a scoring explainer alongside your model to observe explanations during inferencing.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
 
 For more information on the supported interpretability techniques and machine learning models, see [Model interpretability in Azure Machine Learning](how-to-machine-learning-interpretability.md) and [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/explain-model).
 
diff --git a/articles/machine-learning/how-to-manage-optimize-cost.md b/articles/machine-learning/how-to-manage-optimize-cost.md
@@ -33,6 +33,11 @@ Use the following tips to help you manage and optimize your compute resource cos
 
 For information on planning and monitoring costs, see the [plan to manage costs for Azure Machine Learning](concept-plan-manage-cost.md) guide.
 
+> [!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/).
+
 ## Use Azure Machine Learning compute cluster (AmlCompute)
 
 With constantly changing data, you need fast and streamlined model training and retraining to maintain accurate models. However, continuous training comes at a cost, especially for deep learning models on GPUs. 
diff --git a/articles/machine-learning/how-to-manage-registries.md b/articles/machine-learning/how-to-manage-registries.md
@@ -26,6 +26,8 @@ Azure Machine Learning registries (preview) enable you to create and use those a
 
 :::image type="content" source="./media/how-to-manage-registries/machine-learning-registry-block-diagram.png" alt-text="Diagram of the relationships between assets in workspace and registry.":::
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Prerequisites
 
 [!INCLUDE [CLI v2 preres](../../includes/machine-learning-cli-prereqs.md)]
diff --git a/articles/machine-learning/how-to-manage-resources-vscode.md b/articles/machine-learning/how-to-manage-resources-vscode.md
@@ -17,6 +17,8 @@ ms.date: 05/25/2021
 
 Learn how to manage Azure Machine Learning resources with the VS Code extension.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ![Azure Machine Learning VS Code Extension](media/how-to-manage-resources-vscode/azure-machine-learning-vscode-extension.png)
 
 ## Prerequisites
diff --git a/articles/machine-learning/how-to-monitor-online-endpoints.md b/articles/machine-learning/how-to-monitor-online-endpoints.md
@@ -24,6 +24,11 @@ In this article you learn how to:
 > * Create a dashboard for your metrics
 > * Create a metric alert
 
+> [!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
 
 - Deploy an Azure Machine Learning online endpoint.
diff --git a/articles/machine-learning/how-to-responsible-ai-insights-sdk-cli.md b/articles/machine-learning/how-to-responsible-ai-insights-sdk-cli.md
@@ -38,6 +38,11 @@ The `RAI Insights dashboard constructor` and `Gather RAI Insights dashboard` com
 
 In the following sections are specifications of the Responsible AI components and examples of code snippets in YAML and Python. To view the full code, see [sample YAML and Python notebook](https://aka.ms/RAIsamplesProgrammer).
 
+> [!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/).
+
 ### Limitations
 
 The current set of components have a number of limitations on their use:
diff --git a/articles/machine-learning/how-to-responsible-ai-insights-ui.md b/articles/machine-learning/how-to-responsible-ai-insights-ui.md
@@ -17,6 +17,8 @@ ms.custom: responsible-ml, event-tier1-build-2022
 
 In this article, you create a Responsible AI dashboard and scorecard (preview) with a no-code experience in the [Azure Machine Learning studio UI](https://ml.azure.com/).
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 To access the dashboard generation wizard and generate a Responsible AI dashboard, do the following:
 1. [Register your model](how-to-manage-models.md) in Azure Machine Learning so that you can access the no-code experience.
 1. On the left pane of Azure Machine Learning studio, select the **Models** tab.
diff --git a/articles/machine-learning/how-to-responsible-ai-scorecard.md b/articles/machine-learning/how-to-responsible-ai-scorecard.md
@@ -19,6 +19,8 @@ ms.custom: responsible-ml, event-tier1-build-2022
 
 An Azure Machine Learning Responsible AI scorecard is a PDF report that's generated based on Responsible AI dashboard insights and customizations to accompany your machine learning models. You can easily configure, download, and share your PDF scorecard with your technical and non-technical stakeholders to educate them about your data and model health and compliance, and to help build trust. You can also use the scorecard in audit reviews to inform the stakeholders about the characteristics of your model.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Where to find your Responsible AI scorecard
 
  Responsible AI scorecards are linked to your Responsible AI dashboards. To view your Responsible AI scorecard, go into your model registry by selecting the **Model** in Azure Machine Learning studio. Then select the registered model that you've generated a Responsible AI dashboard and scorecard for. After you've selected your model, select the **Responsible AI** tab to view a list of generated dashboards. Select which dashboard you want to export a Responsible AI scorecard PDF for by selecting **Responsible AI Insights** then **View all PDF scorecards.
diff --git a/articles/machine-learning/how-to-schedule-pipeline-job.md b/articles/machine-learning/how-to-schedule-pipeline-job.md
@@ -41,7 +41,10 @@ In this article, you'll learn how to programmatically schedule a pipeline to run
 - To enable this feature:
   1. Navigate to Azure Machine Learning studio UI.
   2. Select **Manage preview features** (megaphone icon) among the icons on the top right side of the screen.
-  3. In **Managed preview feature** panel, toggle on **Create and manage your pipeline schedule** feature.
+  
+    [!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
+  1. In **Managed preview feature** panel, toggle on **Create and manage your pipeline schedule** feature.
     :::image type="content" source="./media/how-to-schedule-pipeline-job/manage-preview-features.png" alt-text="Screenshot of manage preview features toggled on." lightbox= "./media/how-to-schedule-pipeline-job/manage-preview-features.png":::
 
 ---
diff --git a/articles/machine-learning/how-to-secure-training-vnet.md b/articles/machine-learning/how-to-secure-training-vnet.md
@@ -37,6 +37,11 @@ The following table contains the differences between these configurations:
 
 You can also use Azure Databricks or HDInsight to train models in a virtual network.
 
+> [!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/).
+
 > [!TIP]
 > This article is part of a series on securing an Azure Machine Learning workflow. See the other articles in this series:
 >
diff --git a/articles/machine-learning/how-to-set-up-vs-code-remote.md b/articles/machine-learning/how-to-set-up-vs-code-remote.md
@@ -24,6 +24,8 @@ There are two ways you can connect to a compute instance from Visual Studio Code
 * Remote compute instance. This option provides you with a full-featured development environment for building your machine learning projects.
 * Remote Jupyter Notebook server. This option allows you to set a compute instance as a remote Jupyter Notebook server.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Configure a remote compute instance
 
 To configure a remote compute instance for development, you'll need a few prerequisites.
diff --git a/articles/machine-learning/how-to-setup-vs-code.md b/articles/machine-learning/how-to-setup-vs-code.md
@@ -25,6 +25,8 @@ The Azure Machine Learning extension for VS Code provides a user interface to:
 - Debug machine learning experiments locally
 - Schema-based language support, autocompletion and diagnostics for specification file authoring
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Prerequisites
 
 - Azure subscription. If you don't have one, sign up to try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/).
diff --git a/articles/machine-learning/how-to-share-models-pipelines-across-workspaces-with-registries.md b/articles/machine-learning/how-to-share-models-pipelines-across-workspaces-with-registries.md
@@ -28,6 +28,9 @@ In this article, you'll learn how to:
 * Use the component from registry to submit a model training job in a workspace.
 * Register the trained model in the registry.
 * Deploy the model from the registry to an online-endpoint in the workspace, then submit an inference request.
+
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Prerequisites
 
 Before following the steps in this article, make sure you have the following prerequisites:
diff --git a/articles/machine-learning/how-to-track-experiments-mlflow.md b/articles/machine-learning/how-to-track-experiments-mlflow.md
@@ -28,6 +28,11 @@ MLflow allows you to:
 
 In this article, you'll learn how to manage experiments and runs in your workspace using Azure Machine Learning and MLflow SDK in Python.
 
+> [!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/).
+
 ## Using MLflow SDK in Azure Machine Learning
 
 Use MLflow to query and manage all the experiments in Azure Machine Learning. The MLflow SDK has capabilities to query everything that happens inside of a training job in Azure Machine Learning. See [Support matrix for querying runs and experiments in Azure Machine Learning](#support-matrix-for-querying-runs-and-experiments) for a detailed comparison between MLflow Open-Source and MLflow when connected to Azure Machine Learning.
diff --git a/articles/machine-learning/how-to-track-monitor-analyze-runs.md b/articles/machine-learning/how-to-track-monitor-analyze-runs.md
@@ -37,6 +37,11 @@ This article shows how to do the following tasks:
 >
 > If you're looking for information on monitoring models deployed to online endpoints, see [Monitor online endpoints](how-to-monitor-online-endpoints.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
 
 You'll need the following items:
diff --git a/articles/machine-learning/how-to-train-with-ui.md b/articles/machine-learning/how-to-train-with-ui.md
@@ -17,6 +17,8 @@ ms.reviewer: ssalgado
 
 There are many ways to create a training job with Azure Machine Learning. You can use the CLI (see [Train models (create jobs)](how-to-train-model.md)), the REST API (see [Train models with REST (preview)](how-to-train-with-rest.md)), or you can use the UI to directly create a training job. In this article, you'll learn how to use your own data and code to train a machine learning model with the job creation UI in Azure Machine Learning studio.
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Prerequisites
 
 * An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/machine-learning/search/) today.
diff --git a/articles/machine-learning/how-to-understand-automated-ml.md b/articles/machine-learning/how-to-understand-automated-ml.md
@@ -28,6 +28,10 @@ For example, automated ML generates the following charts based on experiment typ
 | [Cumulative gains curve](#cumulative-gains-curve)           |                                                         |
 | [Calibration curve](#calibration-curve)                     |                     
 
+> [!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
 
diff --git a/articles/machine-learning/how-to-use-event-grid.md b/articles/machine-learning/how-to-use-event-grid.md
@@ -25,6 +25,8 @@ When to use Event Grid for event driven actions:
 * Streaming events from Azure Machine Learning to various of endpoints
 * Trigger an ML pipeline when drift is detected
 
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+
 ## Prerequisites
 
 To use Event Grid, you need contributor or owner access to the Azure Machine Learning workspace you will create events for.
diff --git a/articles/machine-learning/how-to-use-pipeline-ui.md b/articles/machine-learning/how-to-use-pipeline-ui.md
diff --git a/articles/machine-learning/migrate-to-v2-managed-online-endpoints.md b/articles/machine-learning/migrate-to-v2-managed-online-endpoints.md
diff --git a/articles/machine-learning/overview-what-is-azure-machine-learning.md b/articles/machine-learning/overview-what-is-azure-machine-learning.md
diff --git a/articles/machine-learning/tutorial-train-deploy-image-classification-model-vscode.md b/articles/machine-learning/tutorial-train-deploy-image-classification-model-vscode.md
diff --git a/articles/machine-learning/v1/concept-model-management-and-deployment.md b/articles/machine-learning/v1/concept-model-management-and-deployment.md
diff --git a/articles/machine-learning/v1/how-to-attach-compute-targets.md b/articles/machine-learning/v1/how-to-attach-compute-targets.md
diff --git a/articles/machine-learning/v1/how-to-configure-environment-v1.md b/articles/machine-learning/v1/how-to-configure-environment-v1.md
diff --git a/articles/machine-learning/v1/how-to-create-register-datasets.md b/articles/machine-learning/v1/how-to-create-register-datasets.md
diff --git a/articles/machine-learning/v1/how-to-machine-learning-interpretability-automl.md b/articles/machine-learning/v1/how-to-machine-learning-interpretability-automl.md
diff --git a/articles/machine-learning/v1/how-to-secure-training-vnet.md b/articles/machine-learning/v1/how-to-secure-training-vnet.md
diff --git a/articles/machine-learning/v1/how-to-train-scikit-learn.md b/articles/machine-learning/v1/how-to-train-scikit-learn.md
diff --git a/articles/machine-learning/v1/how-to-train-tensorflow.md b/articles/machine-learning/v1/how-to-train-tensorflow.md

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`@@ -32,6 +32,7 @@ In this how-to guide, you learn to use the interpretability package of the Azure`
`32`	`32`
`33`	`33`	`* Deploy a scoring explainer alongside your model to observe explanations during inferencing.`
`34`	`34`
	`35`	`+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]`
`35`	`36`
`36`	`37`	`For more information on the supported interpretability techniques and machine learning models, see [Model interpretability in Azure Machine Learning](how-to-machine-learning-interpretability.md) and [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/explain-model).`
`37`	`38`