Update the AzureML article

Author: msakande

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

@@ -117,7 +117,7 @@ For non-Microsoft models offered through the Azure Marketplace, you can deploy t
 
     # [AI Studio](#tab/azure-ai-studio)
 
-    1. On the model's **Details** page, select **Deploy** and then select **Serverless API with Azure AI Content Safety** to open the deployment wizard.
+    1. On the model's **Details** page, select **Deploy** and then select **Serverless API with Azure AI Content Safety (preview)** to open the deployment wizard.
 
     1. Select the project in which you want to deploy your models. To use the serverless API model 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.
 
@@ -244,14 +244,14 @@ Once you've created a subscription for a non-Microsoft model, you can deploy the
 
 The serverless API endpoint 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 article, you create an endpoint with the name **meta-llama3-8b-qwerty**.
+In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
 
 1. Create the serverless endpoint
 
     # [AI Studio](#tab/azure-ai-studio)
 
     1. To deploy a Microsoft model that doesn't require subscribing to a model offering:
-        1. Select **Deploy** and then select **Serverless API with Azure AI Content Safety** to open the deployment wizard.
+        1. Select **Deploy** and then select **Serverless API with Azure AI Content Safety (preview)** to open the deployment wizard.
         1. Select the project in which you want to deploy your model. Notice that not all the regions are supported.
 
     1. Alternatively, for a non-Microsoft model that requires a model subscription, if you've just subscribed your project to the model offer in the previous section, continue to select **Deploy**. Alternatively, select **Continue to deploy** (if your deployment wizard had the note *You already have an Azure Marketplace subscription for this project*).

articles/machine-learning/how-to-deploy-models-serverless.md

@@ -6,8 +6,9 @@ manager: scottpolly
 ms.service: machine-learning
 ms.subservice: inferencing
 ms.topic: how-to
-ms.date: 05/09/2024
-ms.reviewer: None
+ms.date: 07/19/2024
+ms.reviewer: fasantia
+reviewer: santiagxf
 ms.author: mopeakande
 author: msakande
 ms.custom: build-2024, serverless, devx-track-azurecli
@@ -17,7 +18,7 @@ ms.custom: build-2024, serverless, devx-track-azurecli
 
 In this article, you learn how to deploy a model from the model catalog as a serverless API with pay-as-you-go token-based billing.
 
-Certain models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. 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](concept-endpoint-serverless-availability.md) can be deployed as a serverless API with pay-as-you-go billing. 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.
 
 ## Prerequisites
 
@@ -82,18 +83,15 @@ Certain models in the model catalog can be deployed as a serverless API with pay
     You can use any compatible web browser to [deploy ARM templates](../azure-resource-manager/templates/deploy-portal.md) in the Microsoft Azure portal or using any of the deployment tools. This tutorial uses the [Azure CLI](/cli/azure/).
 
 
-## Subscribe your workspace to the model offering
-
-For models offered through the Azure Marketplace, you can deploy them to serverless API endpoints to consume their predictions. If it's your first time deploying the model in the workspace, you have to subscribe your workspace for the particular model offering from the Azure Marketplace. Each workspace has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending.
-
-> [!NOTE]
-> Models offered through the Azure Marketplace are available for deployment to serverless API endpoints in specific regions. Check [Region availability for models in Serverless API endpoints](concept-endpoint-serverless-availability.md) to verify which regions are available. If the one you need is not listed, you can deploy to a workspace in a supported region and then [consume serverless API endpoints from a different workspace](how-to-connect-models-serverless.md).
+## Find your model and model ID in the model catalog
 
 1. Sign in to [Azure Machine Learning studio](https://ml.azure.com)
 
-1. Ensure 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).
+1. For models offered through the Azure Marketplace, 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).
+
+    Models that are offered by non-Microsoft providers (for example, Llama and Mistral models) are billed through the Azure Marketplace. For such models, you're required to subscribe your project to the particular model offering. Models that are offered by Microsoft (for example, Phi-3 models) don't have this requirement, as billing is done differently. For details about billing for serverless deployment of models in the model catalog, see [Billing for serverless APIs](concept-model-catalog.md#pay-for-model-usage-in-maas).
 
-1. Go to your workspace.
+1. Go to your workspace. To use the serverless API model deployment offering, your workspace must belong to one of the [regions that are supported for serverless deployment](concept-endpoint-serverless-availability.md) for the particular model you want to deploy.
 
 1. Select **Model catalog** from the left sidebar and find the model card of the model you want to deploy. In this article, you select a **Meta-Llama-3-8B-Instruct** model. 
     
@@ -104,12 +102,20 @@ For models offered through the Azure Marketplace, you can deploy them to serverl
 
     :::image type="content" source="media/how-to-deploy-models-serverless/model-card.png" alt-text="A screenshot showing a model's details page." lightbox="media/how-to-deploy-models-serverless/model-card.png":::
 
+The next section covers the steps for subscribing your project to a model offering. You can skip this section and go to [Deploy the model to a serverless API endpoint](#deploy-the-model-to-a-serverless-api-endpoint), if you're deploying a Microsoft model.
+
+## Subscribe your project to the model offering
+
+For non-Microsoft models offered through the Azure Marketplace, you can deploy them to serverless API endpoints to consume their predictions. If it's your first time deploying the model in the project, you have to subscribe your workspace for the particular model offering from the Azure Marketplace. Each workspace has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending.
+
+> [!NOTE]
+> Models offered through the Azure Marketplace are available for deployment to serverless API endpoints in specific regions. Check [Region availability for models in serverless API endpoints](concept-endpoint-serverless-availability.md) to verify which models and regions are available. If the one you need is not listed, you can deploy to a workspace in a supported region and then [consume serverless API endpoints from a different workspace](how-to-connect-models-serverless.md).
 
 1. Create the model's marketplace subscription. When you create a subscription, you accept the terms and conditions associated with the model offer.
 
     # [Studio](#tab/azure-studio)
 
-    1. On the model's **Details** page, select **Deploy** and then select **Serverless API** to open the deployment wizard.
+    1. On the model's **Details** page, select **Deploy** and then select **Serverless API with Azure AI Content Safety (preview)** to open the deployment wizard.
 
     1. Select the checkbox to acknowledge the Microsoft purchase policy.    
 
@@ -194,7 +200,7 @@ For models offered through the Azure Marketplace, you can deploy them to serverl
     }
     ```
 
-1. Once you sign up the workspace for the particular Azure Marketplace offering, subsequent deployments of the same offering in the same workspace don't require subscribing again.
+1. Once you subscribe the workspace for the particular Azure Marketplace offering, subsequent deployments of the same offering in the same workspace don't require subscribing again.
 
 1. At any point, you can see the model offers to which your workspace is currently subscribed:
 
@@ -236,15 +242,19 @@ For models offered through the Azure Marketplace, you can deploy them to serverl
 
 ## Deploy the model to a serverless API endpoint
 
-Once you've created a model's subscription, you can deploy the associated model to a serverless API endpoint. The serverless API endpoint 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.
+Once you've created a subscription for a non-Microsoft model, you can deploy the associated model to a serverless API endpoint. For Microsoft models (such as Phi-3 models), you don't need to create a subscription.
 
-In this article, you create an endpoint with name **meta-llama3-8b-qwerty**.
+The serverless API endpoint 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 with the name **meta-llama3-8b-qwerty**.
 
 1. Create the serverless endpoint
 
     # [Studio](#tab/azure-studio)
 
-    1. From the previous wizard, select **Deploy** (if you've just subscribed the workspace to the model offer in the previous section), or select **Continue to deploy** (if your deployment wizard had the note *You already have an Azure Marketplace subscription for this workspace*). 
+    1. To deploy a Microsoft model that doesn't require subscribing to a model offering, select **Deploy** and then select **Serverless API with Azure AI Content Safety (preview)** to open the deployment wizard.
+
+    1. Alternatively, for a non-Microsoft model that requires a model subscription, if you've just subscribed your project to the model offer in the previous section, continue to select **Deploy**. Alternatively, select **Continue to deploy** (if your deployment wizard had the note *You already have an Azure Marketplace subscription for this workspace*). 
 
         :::image type="content" source="media/how-to-deploy-models-serverless/deploy-pay-as-you-go-subscribed-workspace.png" alt-text="A screenshot showing a workspace that is already subscribed to the offering." lightbox="media/how-to-deploy-models-serverless/deploy-pay-as-you-go-subscribed-workspace.png":::
 
@@ -422,11 +432,11 @@ In this article, you create an endpoint with name **meta-llama3-8b-qwerty**.
     > [!TIP]
     > If you're using prompt flow in the same workspace where the deployment was deployed, you still need to create the connection.
 
-## Using the serverless API endpoint
+## Use the serverless API endpoint
 
 Models deployed in Azure Machine Learning and Azure AI studio in Serverless API endpoints support the [Azure AI Model Inference API](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](reference-model-inference-api.md#capabilities) and how [you can leverage it when building applications](reference-model-inference-api.md#getting-started). 
+Read more about the [capabilities of this API](reference-model-inference-api.md#capabilities) and how [you can use it when building applications](reference-model-inference-api.md#getting-started). 
 
 ## Delete endpoints and subscriptions
 
@@ -501,15 +511,22 @@ az resource delete --name <resource-name>
 
 ## Cost and quota considerations for models deployed as serverless API endpoints
 
-Models deployed as a serverless API endpoint are offered through the Azure Marketplace and integrated with Azure Machine Learning for use. You can find the Azure Marketplace pricing when deploying or fine-tuning the models.
+Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per workspace. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.
+
+#### Cost for Microsoft models
+
+You can find the pricing information on the __Pricing and terms__ tab of the deployment wizard when deploying Microsoft models (such as Phi-3 models) as serverless API endpoints.
+
+#### Cost for non-Microsoft models
+
+Non-Microsoft models deployed as serverless API endpoints are offered through the Azure Marketplace and integrated with Azure AI Studio for use. You can find the Azure Marketplace pricing when deploying or fine-tuning these models.
 
 Each time a workspace subscribes to a given offer from the Azure Marketplace, a new resource is created to track the costs associated with its consumption. The same resource is used to track costs associated with inference and fine-tuning; however, multiple meters are available to track each scenario independently.
 
 For more information on how to track costs, see [Monitor costs for models offered through the Azure Marketplace](../ai-studio/how-to/costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace).
 
 :::image type="content" source="media/how-to-deploy-models-serverless/costs-model-as-service-cost-details.png" alt-text="A screenshot showing different resources corresponding to different model offers and their associated meters." lightbox="media/how-to-deploy-models-serverless/costs-model-as-service-cost-details.png":::
 
-Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per workspace. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.
 
 ## Permissions required to subscribe to model offerings