Merge pull request #322 from MicrosoftDocs/main

9/17/2024 AM Publish

Author: Taojunshen

articles/ai-studio/how-to/quota.md

@@ -93,13 +93,25 @@ Azure Storage has a limit of 250 storage accounts per region, per subscription.
 
 Use quotas to manage compute target allocation between multiple Azure AI Studio hubs in the same subscription. 
 
-By default, all hubs share the same quota as the subscription-level quota for VM families. However, you can set a maximum quota for individual VM families for more granular cost control and governance on hubs in a subscription. Quotas for individual VM families let you share capacity and avoid resource contention issues. 
+By default, all hubs share the same quota as the subscription-level quota for VM families. However, you can set a maximum quota for individual VM families for more granular cost control and governance on hubs in a subscription. Quotas for individual VM families let you share capacity and avoid resource contention issues.
 
-1. In Azure AI Studio, go to the **Home** page and select **Quota**. 
+1. In Azure AI Studio, go to the **Home** page and select either **Model quota** or **VM quota** from the **Management** section.
 
-1. Select the **Azure ML** tab to view the quota for the VM families. The quota is displayed at the subscription level in the selected Azure region. To request more quota, select the VM family and then select **Request quota**. 
+    :::image type="content" source="../media/cost-management/select-model-vm-quota.png" alt-text="Screenshot of the Model and VM quota entries in the management section." lightbox="../media/cost-management/select-model-vm-quota.png":::
 
-    :::image type="content" source="../media/cost-management/quota-manage.png" alt-text="Screenshot of the page to view and request quota for VM families in Azure AI Studio." lightbox="../media/cost-management/quota-manage.png":::
+1. When you select **Model quota**, you can view the quota for the models in the selected Azure region. To request more quota, select the model and then select **Request quota**. 
+
+    - Use the **Show all quota** toggle to display all quota or only the currently allocated quota.
+    - Use the **Group by** dropdown to group the list by **Quota type, Region & Model**, **Quota type, Model & Region**, or **None**. The **None** grouping displays a list of model deployments.
+    - Expand the groupings to view information on specific model deployments. While viewing a model deployment, select the **pencil icon** in the **Quota allocation** column to edit the quota allocation for the model deployment.
+    - Use the **charts** along the side of the page to view more details about quota usage. The charts are interactive; hovering over a section of the chart displays more information, and selecting the chart filters the list of models. Selecting the chart legend filters the data displayed in the chart.
+    - Use the **Azure OpenAI Provisioned** link to view information about provisioned models, including a **Capacity calculator**.
+  
+    :::image type="content" source="../media/cost-management/model-quota.png" alt-text="Screenshot of the Model quota page in Azure AI Studio." lightbox="../media/cost-management/model-quota.png":::
+
+1. When you select **VM quota**, you can view the quota and usage for the virtual machine families in the selected Azure region. To request more quota, select the VM family and then select **Request quota**. 
+
+    :::image type="content" source="../media/cost-management/vm-quota.png" alt-text="Screenshot of the VM quota page in Azure AI Studio." lightbox="../media/cost-management/vm-quota.png":::
 
 ## Next steps 
 

articles/ai-studio/media/cost-management/model-quota.png

@@ -8,26 +8,25 @@ ms.author: sgilley
 ms.reviewer: vijetaj
 ms.service: azure-machine-learning
 ms.subservice: compute
-ms.date: 03/04/2024
+ms.date: 09/17/2024
 ms.topic: how-to
 ms.custom: build-2023
+# customer intent: As a professional data scientist, I want to manage compute resources for model training and deployment in Azure Machine Learning studio.
 ---
 
 # Manage compute resources for model training and deployment in studio
 
-In this article, learn how to manage the compute resources you use for model training and deployment in Azure Machine studio.  
+In this article, learn how to manage the compute resources you use for model training and deployment in Azure Machine studio. 
+
+With Azure Machine Learning, you can train your model on various resources or environments, collectively referred to as _compute targets_). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine. 
+
+You can also use [serverless compute](./how-to-use-serverless-compute.md) as a compute target. There's nothing for you to manage when you use serverless compute.
 
 ## Prerequisites
 
 * 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/) today
 * An [Azure Machine Learning workspace](quickstart-create-resources.md)
 
-## What's a compute target?
-
-With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as _compute targets_). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine. 
-
-You can also use [serverless compute](./how-to-use-serverless-compute.md) as a compute target.  There's nothing for you to manage when you use serverless compute.
-
 ## View compute targets
 
 To see all compute targets for your workspace, use the following steps:
@@ -40,9 +39,7 @@ To see all compute targets for your workspace, use the following steps:
 
     :::image type="content" source="media/how-to-create-attach-studio/compute-targets.png" alt-text="Screenshot of view list of compute targets." lightbox="media/how-to-create-attach-studio/compute-targets.png":::
 
-[!INCLUDE [retiring vms](./includes/retiring-vms.md)]
-
-## Compute instance and clusters
+## Create compute instance and clusters
 
 You can create compute instances and compute clusters in your workspace, using the Azure Machine Learning SDK, CLI, or studio:
 
@@ -51,19 +48,19 @@ You can create compute instances and compute clusters in your workspace, using t
 
 In addition, you can use the [VS Code extension](how-to-manage-resources-vscode.md#compute-clusters) to create compute instances and compute clusters in your workspace.
 
-## Kubernetes clusters
+## Attach Kubernetes clusters
 
 For information on configuring and attaching a Kubernetes cluster to your workspace, see [Configure Kubernetes cluster for Azure Machine Learning](how-to-attach-kubernetes-anywhere.md).
 
-## Other compute targets
+## <a name="other-compute-targets"></a>Attach other compute targets
 
-To use VMs created outside the Azure Machine Learning workspace, you must first attach them to your workspace. Attaching the compute resource makes it available to your workspace.  
+To use VMs created outside the Azure Machine Learning workspace, you must first attach them to your workspace. Attaching the compute resource makes it available to your workspace. 
 
 1. Navigate to [Azure Machine Learning studio](https://ml.azure.com).
 
 1. Under __Manage__, select __Compute__.
 
-1. In the tabs at the top, select **Attached compute** to attach a compute target for **training**.  
+1. In the tabs at the top, select **Attached compute** to attach a compute target for **training**. 
 
 1. Select +New, then select the type of compute to attach. Not all compute types can be attached from Azure Machine Learning studio.
 
@@ -78,7 +75,7 @@ To use VMs created outside the Azure Machine Learning workspace, you must first
 1. Select __Attach__.
 
 
-To detach your compute use the following steps:
+Detach your compute with the following steps:
 
 1. In Azure Machine Learning studio, select __Compute__, __Attached compute__, and the compute you wish to remove.
 1. Use the __Detach__ link to detach your compute.
@@ -87,9 +84,8 @@ To detach your compute use the following steps:
 
 [!INCLUDE [ssh-access](includes/machine-learning-ssh-access.md)]
 
-## Next steps
+## Related content
 
 * Use the compute resource to [submit a training run](how-to-train-model.md).
-* Learn how to [efficiently tune hyperparameters](how-to-tune-hyperparameters.md) to build better models.
 * Once you have a trained model, learn [how and where to deploy models](how-to-deploy-online-endpoints.md).
 * [Use Azure Machine Learning with Azure Virtual Networks](./how-to-network-security-overview.md)