implement peer review feedback and freshness

Author: msakande

articles/ai-foundry/how-to/deploy-models-serverless.md

@@ -1,7 +1,7 @@
 ---
-title: Deploy models as standard deployments
+title: Deploy models as serverless API deployments
 titleSuffix: Azure AI Foundry
-description: Learn to deploy models as standard deployments, using Azure AI Foundry.
+description: Learn to deploy models as serverless API deployments, using Azure AI Foundry.
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: how-to
@@ -11,39 +11,154 @@ author: msakande
 ms.reviewer: fasantia
 reviewer: santiagxf
 ms.custom: build-2024, serverless, devx-track-azurecli, ignite-2024
+zone_pivot_groups: azure-ai-serverless-deployment
 ---
 
-# Deploy models as standard deployments
+# Deploy models as serverless API deployments
 
-In this article, you learn how to deploy a model from the model catalog as a standard deployment.
+In this article, you learn how to deploy a model from the model catalog as a serverless API deployment.
 
 [!INCLUDE [models-preview](../includes/models-preview.md)]
 
-[Certain models in the model catalog](deploy-models-serverless-availability.md) can be deployed as a standard deployment. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
+[Certain models in the model catalog](deploy-models-serverless-availability.md) can be deployed as a serverless API deployment. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
 
-This article uses a Meta Llama model deployment for illustration. However, you can use the same steps to deploy any of the [models in the model catalog that are available for standard deployment](deploy-models-serverless-availability.md).
+<!-- This article uses a Meta Llama model deployment for illustration. However, you can use the same steps to deploy any of the [models in the model catalog that are available for serverless API deployment](deploy-models-serverless-availability.md). -->
 
 ## Prerequisites
 
 - An Azure subscription with a valid payment method. Free or trial Azure subscriptions won't work. If you don't have an Azure subscription, create a [paid Azure account](https://azure.microsoft.com/pricing/purchase-options/pay-as-you-go) to begin.
 
 - If you don't have one, [create a [!INCLUDE [hub](../includes/hub-project-name.md)]](create-projects.md?pivots=hub-project).
 
-- Ensure that the **Deploy models to Azure AI model inference service** feature is turned off in the Azure AI Foundry portal. When this feature is on, standard deployments are not available for deployment when using the portal.
+- Ensure that the **Deploy models to Azure AI Foundry resources** feature is turned off in the Azure AI Foundry portal. When this feature is on, serverless API deployments are not available from the portal.
 
-    :::image type="content" source="../media/deploy-models-serverless/ai-project-inference-endpoint.gif" alt-text="An animation showing how to turn off the Deploy models to Azure AI model inference service feature in Azure AI Foundry portal." lightbox="../media/deploy-models-serverless/ai-project-inference-endpoint.gif":::
+    :::image type="content" source="../media/deploy-models-serverless/foundry-resources-deployment-disabled.gif" alt-text="A screenshot of the Azure AI Foundry portal showing where to disable deployment to Azure AI Foundry resources." lightbox="../media/deploy-models-serverless/foundry-resources-deployment-disabled.gif":::
 
 - Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Foundry portal. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-azure-ai-foundry.md).
 
-- You need to install the following software to work with Azure AI Foundry:
+::: zone pivot="ai-foundry-portal"
+
+- You can use any compatible web browser to navigate [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs).
+
+## Find your model in the model catalog
+
+[!INCLUDE [open-catalog](../includes/open-catalog.md)]
+
+# [Models sold directly by Azure](#tab/azure-direct)
+
+4. Select the model card of the model you want to deploy. In this article, you select a **DeepSeek-R1-0528** model.
+1. Select **Use this model** and view the *Pricing and terms* tab in the window that opens.
+1. Select **Agree and Proceed** to open the deployment wizard. Here, you can name the deployment and select the deployment type.
+    :::image type="content" source="../media/deploy-models-serverless/deepseek-deployment-wizard.png" alt-text="Screenshot showing the deployment wizard for a model sold directly by Azure." lightbox="../media/deploy-models-serverless/deepseek-deployment-wizard.png":::
+    
+   
+# [Models from Partners and Community](#tab/partner-models)
+
+> [!NOTE]
+> [Models from Partners and Community](../concepts/foundry-models-overview.md#models-from-partners-and-community) are offered through the Azure Marketplace. For these models, ensure that your account has the **Azure AI Developer** role permissions on the resource group, or that you meet the [permissions required to subscribe to model offerings](#permissions-required-to-subscribe-to-model-offerings), as you're required to subscribe your project to the particular model offering.
+
+4. Select the model card of the model you want to deploy. In this article, you select the **AI21-Jamba-1.5-Large** model.
+    
+The next section covers the steps for subscribing your project to a model offering.
+
+### Subscribe your project to the model offering
+
+Standard deployments can deploy both Microsoft and non-Microsoft offered models. For models from partners and community, e.g., the Gretel model, you must create a subscription before you can deploy them. If it's your first time deploying the model in the project, you have to subscribe your project for the particular model offering from the Azure Marketplace. Each project has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending.
+
+Furthermore, models offered through the Azure Marketplace are available for deployment to standard deployment in specific regions. Check [Model and region availability for standard deployment](deploy-models-serverless-availability.md) to verify which models and regions are available. If the one you need is not listed, you can deploy to a project in a supported region and then [consume standard deployment from a different project](deploy-models-serverless-connect.md).
+
+1. Create the model's marketplace subscription. When you create a subscription, you accept the terms and conditions associated with the model offer.
 
     # [Azure AI Foundry portal](#tab/azure-ai-studio)
 
-    You can use any compatible web browser to navigate [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs).
+    1. On the model's **Details** page, select **Use this model** to open the Serverless API deployment window. In the Serverless API deployment window, the **Azure Marketplace Terms** link provides more information about the terms of use. The **Pricing and terms** tab also provides pricing details for the selected model.
+
+        > [!TIP]
+        > For models that can be deployed via serverless API deployment or managed compute, a **Deployment options** window opens up, giving you the choice between serverless API deployment and deployment using a managed compute. From there, you can select the serverless API deployment option.
+        > 
+        > To use the serverless API deployment offering, your project must belong to one of the [regions that are supported for serverless deployment](deploy-models-serverless-availability.md) for the particular model.
+        
+    1. If you've never deployed the model in your project before, you first have to subscribe to the model's offering in the Azure Marketplace. Select **Subscribe and Deploy** to open the deployment wizard. 
+        :::image type="content" source="../media/deploy-models-serverless/model-marketplace-subscription.png" alt-text="Screenshot showing where to subscribe a model to the Azure marketplace before deployment." lightbox="../media/deploy-models-serverless/model-marketplace-subscription.png":::
+
+    1. Alternatively, if you see the note *You already have an Azure Marketplace subscription for this project*, you don't need to create the subscription since you already have one. Select **Continue to deploy** to open the deployment wizard. 
+        :::image type="content" source="../media/deploy-models-serverless/model-subscribed-to-marketplace.png" alt-text="Deployment page for a model that is already subscribed to Azure marketplace." lightbox="../media/deploy-models-serverless/model-subscribed-to-marketplace.png":::    
+
+    1. (Optional) Once you subscribe a project for the particular Azure Marketplace offering, subsequent deployments of the same offering in the same project don't require subscribing again. At any point, you can see the model offers to which your project is currently subscribed:
+      
+        1. Go to the [Azure portal](https://portal.azure.com).
+        1. Navigate to the resource group where the project belongs.
+        1. On the **Type** filter, select **SaaS**.
+        1. You see all the offerings to which you're currently subscribed.
+        1. Select any resource to see the details.
+
+    1. In the deployment wizard, name the deployment. The **Content filter (preview)** option is enabled by default. Leave the default setting for the service to detect harmful content such as hate, self-harm, sexual, and violent content. For more information about content filtering, see [Content filtering in Azure AI Foundry portal](../concepts/content-filtering.md).
+
+---
+
+## Deploy the model to a serverless API and use the deployment
+
+The serverless API deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance organizations need. This deployment option doesn't require quota from your subscription. In this section, you create an endpoint for your model.
+
+1. In the deployment wizard, select **Deploy**. Wait until the deployment is ready and you're redirected to the Deployments page.
+
+1. To see the endpoints deployed to your project, in the **My assets** section of the left pane, select **Models + endpoints**.
+
+1. The created endpoint uses key authentication for authorization. To get the keys associated with a given endpoint, follow these steps:
+    1. Select the deployment, and note the endpoint's Target URI and Key. 
+    1. Use these credentials to call the deployment and generate predictions.
+
+1. If you need to consume this deployment from a different project or hub, or you plan to use prompt flow to build intelligent applications, you need to create a connection to the serverless API deployment. To learn how to configure an existing standard deployment on a new project or hub, see [Consume deployed standard deployment from a different project or from Prompt flow](deploy-models-serverless-connect.md).
+
+    > [!TIP]
+    > If you're using prompt flow in the same project or hub where the deployment was deployed, you still need to create the connection.
+
+### Use the standard deployment
+
+Models deployed in Azure Machine Learning and Azure AI Foundry in standard deployments support the [Azure AI Foundry Models API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md) that exposes a common set of capabilities for foundational models and that can be used by developers to consume predictions from a diverse set of models in a uniform and consistent way. 
+
+Read more about the [capabilities of this API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md#capabilities) and how [you can use it when building applications](../../ai-foundry/model-inference/reference/reference-model-inference-api.md#getting-started). 
+
+
+## Delete endpoints and subscriptions
+
+[!INCLUDE [tip-left-pane](../includes/tip-left-pane.md)]
+
+You can delete model subscriptions and endpoints. Deleting a model subscription makes any associated endpoint become *Unhealthy* and unusable.
+
+To delete a standard deployment:
+
+1. Go to the [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs).
+
+1. Go to your project.
+
+1. In the **My assets** section, select **Models + endpoints**.
+
+1. Open the deployment you want to delete.
+
+1. Select **Delete**.
+
+
+To delete the associated model subscription:
+
+1. Go to the [Azure portal](https://portal.azure.com)
+
+1. Navigate to the resource group where the project belongs.
+
+1. On the **Type** filter, select **SaaS**.
+
+1. Select the subscription you want to delete.
+
+1. Select **Delete**.
 
-    # [Azure CLI](#tab/cli)
 
-    The [Azure CLI](/cli/azure/) and the [ml extension for Azure Machine Learning](/azure/machine-learning/how-to-configure-cli).
+ 
+::: zone-end
+
+
+::: zone pivot="programming-language-cli"
+
+- To work with Azure AI Foundry, install the [Azure CLI](/cli/azure/) and the [ml extension for Azure Machine Learning](/azure/machine-learning/how-to-configure-cli).
 
     ```azurecli
     az extension add -n ml
@@ -62,9 +177,12 @@ This article uses a Meta Llama model deployment for illustration. However, you c
     az configure --defaults workspace=<project-name> group=<resource-group> location=<location>
     ```
 
-    # [Python SDK](#tab/python)
+::: zone-end
+
 
-    Install the [Azure Machine Learning SDK for Python](https://aka.ms/sdk-v2-install).
+::: zone pivot="python-sdk"
+
+- To work with Azure AI Foundry, install the [Azure Machine Learning SDK for Python](https://aka.ms/sdk-v2-install).
 
     ```python
     pip install -U azure-ai-ml
@@ -85,9 +203,12 @@ This article uses a Meta Llama model deployment for illustration. However, you c
     )
     ```
 
-    # [Bicep](#tab/bicep)
+::: zone-end
+
 
-    Install the Azure CLI as described at [Azure CLI](/cli/azure/).
+::: zone pivot="programming-language-bicep"
+
+- To work with Azure AI Foundry, install the Azure CLI as described at [Azure CLI](/cli/azure/).
 
     Configure the following environment variables according to your settings:
 
@@ -96,11 +217,13 @@ This article uses a Meta Llama model deployment for illustration. However, you c
     LOCATION="eastus2" 
     ```  
 
-    # [ARM](#tab/arm)
+::: zone-end
 
-    You can use any compatible web browser to [deploy ARM templates](/azure/azure-resource-manager/templates/deploy-portal) in the Microsoft Azure portal or use any of the deployment tools. This tutorial uses the [Azure CLI](/cli/azure/).
 
 
+
+<!-- 
+
 ## Find your model and model ID in the model catalog
 
 [!INCLUDE [open-catalog](../includes/open-catalog.md)]
@@ -651,7 +774,7 @@ You can use the resource management tools to manage the resources. The following
 az resource delete --name <resource-name>
 ```
 
----
+--- -->
 
 ## Cost and quota considerations for models deployed as a standard deployment
 
@@ -693,6 +816,8 @@ Azure role-based access controls (Azure RBAC) are used to grant access to operat
 
 For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-azure-ai-foundry.md).
 
+
+
 ## Related content
 
 * [Region availability for models as standard deployments](deploy-models-serverless-availability.md)

articles/ai-foundry/media/deploy-models-serverless/ai-project-inference-endpoint.gif

@@ -670,6 +670,20 @@ groups:
       title: Azure CLI
     - id: programming-language-bicep
       title: Bicep
+
+- id: azure-ai-serverless-deployment
+# Owner: mopeakande
+  title: Programming languages
+  prompt: Choose a tool or API
+  pivots:
+    - id: ai-foundry-portal
+      title: Azure AI Foundry portal
+    - id: programming-language-cli
+      title: Azure CLI
+    - id: python-sdk
+      title: Python SDK
+    - id: programming-language-bicep
+      title: Bicep
 - id: azure-ai-model-catalog-sub-group-samples
 # Owner: mopeakande
   title: Programming languages