review of PM's edits

Author: msakande

articles/ai-foundry/concepts/deployments-overview.md

@@ -1,7 +1,7 @@
 ---
-title: Deploy models in Azure AI Foundry portal
+title: Deployment options for Azure AI Foundry Models
 titleSuffix: Azure AI Foundry
-description: Learn about deploying models in Azure AI Foundry portal.
+description: Learn about deployment options for Azure AI Foundry Models.
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: concept-article
@@ -11,56 +11,62 @@ ms.author: mopeakande
 author: msakande
 ---
 
-# Overview: Deploy AI models in Azure AI Foundry
+# Deployment overview for Azure AI Foundry Models
 
-The model catalog in Azure AI Foundry is the hub to discover and use a wide range of models for building generative AI applications. Models need to be deployed to make them available for receiving inference requests. Azure AI Foundry offers a comprehensive suite of deployment options for models, depending on your needs and model requirements.
+The model catalog in Azure AI Foundry is the hub to discover and use a wide range of Foundry Models for building generative AI applications. Models need to be deployed to make them available for receiving inference requests. Azure AI Foundry offers a comprehensive suite of deployment options for Foundry Models, depending on your needs and model requirements.
 
 ## Deployment options
 
-Azure AI Foundry provides multiple deployment options depending on the type of resources and models that you need to provision. The following 3 deployment options are available:
+Azure AI Foundry provides multiple deployment options depending on the type of models and resources you need to provision. The following deployment options are available:
 
-### Standard deployments in Azure AI Foundry resources
+- Standard deployment in Azure AI Foundry resources
+- Deployment to serverless API endpoint
+- Deployment to managed compute
 
-Formerly known Azure AI model inference in Azure AI Services, is **the preferred deployment option** in Azure AI Foundry. It offers the biggest range of options including regional, data zone, or global processing; and standard and provisioned (PTU) options. Flagship models in Azure AI Foundry Models support this deployment option.
+### Standard deployment in Azure AI Foundry resources
+
+Azure AI Foundry resources (formerly referred to as Azure AI model inference, in Azure AI Services), is **the preferred deployment option** in Azure AI Foundry. It offers the widest range of options including regional, data zone, or global processing, and it offers standard and [provisioned throughput (PTU)](../../ai-services/openai/concepts/provisioned-throughput.md) options. Flagship models in Azure AI Foundry Models support this deployment option.
 
 This deployment option is available in:
 
 * Azure OpenAI resources<sup>1</sup>
-* Azure AI Foundry resources (formerly known Azure AI Services)
-* Azure AI Hub when connected to an Azure AI Foundry resource (requires the feature [Deploy models to Azure AI Foundry resources](#configure-azure-ai-foundry-portal-for-deployment-options) on).
+* Azure AI Foundry resources
+* Azure AI hub, when connected to an Azure AI Foundry resource (requires the [Deploy models to Azure AI Foundry resources](#configure-azure-ai-foundry-portal-for-deployment-options) feature to be turned on).
+
+<sup>1</sup>If you're using Azure OpenAI resources, the model catalog only shows Azure OpenAI in Foundry Models for deployment. You can get the full list of Foundry Models by upgrading to an Azure AI Foundry resource.
 
-<sup>1</sup>If you are using Azure OpenAI resources, the model catalog only shows Azure OpenAI models for deployment. You can get the full list of models by upgrading to an Azure AI Foundry resource.
+To get started with standard deployment in Azure AI Foundry resources, see [How-to: Deploy models to Azure AI Foundry Models](../model-inference/how-to/create-model-deployments.md).
 
-To get started, see [How-to: Deploy models to Azure AI Foundry Models](../model-inference/how-to/create-model-deployments.md).
+### Serverless API endpoint
 
-### Serverless API Endpoint
+This option is available **only in** [Azure AI hub resources](ai-resources.md) and it allows the creation of dedicated endpoints to host the model, accessible via API. Azure AI Foundry Models support serverless API endpoints with pay-as-you-go billing. 
 
-This option is available **only in Azure AI Hubs resources** and it allows the creation of dedicated endpoints to host the model, accessible via API with pay-as-you-go billing. It's supported by Azure AI Foundry Models with pay-as-you-go billing. Only regional deployments can be created for Serverless API Endpoints. It requires the feature [Deploy models to Azure AI Foundry resources](#configure-azure-ai-foundry-portal-for-deployment-options) **off**.
+Only regional deployments can be created for serverless API endpoints, and to use it, you _must_ **turn off** the "Deploy models to Azure AI Foundry resources" option.
 
-To get started, see [How-to: Deploy models to Serverless API Endpoints](../model-inference/how-to/create-model-deployments.md)
+To get started with deployment to a serverless API endpoint, see [Deploy models as serverless API deployments](../how-to/deploy-models-serverless.md).
 
 ### Managed Compute
 
-This option is available **only in Azure AI Hubs resources** and it allows the creation of dedicated endpoint to host the model in **dedicated compute**. You need to have compute quota in your subscription to host the model and you are billed per compute up-time. 
+This option is available **only in** [Azure AI hub resources](ai-resources.md) and it allows the creation of a dedicated endpoint to host the model in a **dedicated compute**. You need to have compute quota in your subscription to host the model and you're billed per compute uptime. 
 
-This option is required for the following model collections:
+This deployment option is required for model collections such as these:
 
 * Hugging Face
-* NVIDIA NIMs
+* NVIDIA inference microservices (NIMs)
 * Industry models (Saifr, Rockwell, Bayer, Cerence, Sight Machine, Page AI, SDAIA)
 * Databricks
 * Custom models
 
-To get started, see [How-to: Deploy to Managed compute](../how-to/deploy-models-managed.md).
+To get started, see [How to deploy and inference a managed compute deployment](../how-to/deploy-models-managed.md) and [Deploy Azure AI Foundry Models to managed compute with pay-as-you-go billing](../how-to/deploy-models-managed-pay-go.md).
 
-## Features
+## Capabilities for the deployment options
 
-We recommend using Standard deployments in Azure AI Foundry resources (formerly known Azure AI model inference in Azure AI Services) whenever possible as it offers the larger set of features. The following table shows details about specific features available on each deployment option:
+We recommend using [Standard deployments in Azure AI Foundry resources](#standard-deployment-in-azure-ai-foundry-resources) whenever possible, as it offers the largest set of capabilities among the available deployment options. The following table lists details about specific capabilities available for each deployment option:
 
-| Feature                       | Azure OpenAI | Azure AI Foundry | Serverless API Endpoint | Managed compute |
+| Capability                    | Azure OpenAI | Azure AI Foundry | Serverless API Endpoint | Managed compute |
 |-------------------------------|----------------------|-------------------|----------------|-----------------|
 | Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Foundry Models with pay-as-you-go billing](../../ai-foundry/model-inference/concepts/models.md) | [Foundry Models with pay-as-you-go billing](../how-to/model-catalog-overview.md) | [Open and custom models](../how-to/model-catalog-overview.md#availability-of-models-for-deployment-as-managed-compute) |
-| Deployment resource           | Azure OpenAI resource | Azure AI Foundry resource (formerly known Azure AI Services) | AI project (in AI Hub resource) | AI project (in AI Hub resource) |
+| Deployment resource           | Azure OpenAI resource | Azure AI Foundry resource  | AI project (in AI Hub resource) | AI project (in AI Hub resource) |
 | Requires AI Hubs              | No | No | Yes | Yes |
 | Data processing options       | Regional <br /> Data-zone  <br /> Global | Regional <br /> Data-zone  <br /> Global | Regional | Regional |
 | Private networking            | Yes | Yes | Yes | Yes |
@@ -75,13 +81,11 @@ We recommend using Standard deployments in Azure AI Foundry resources (formerly
 
 ## Configure Azure AI Foundry portal for deployment options
 
-Azure AI Foundry portal may automatically pick up a deployment option based on your environment and configuration. When possible, we default to the most convenient deployment option available to you.
-
-We recommend using Azure AI Foundry resources (formerly known Azure AI Services) for deployment whenever possible. To do that, ensure you have the feature **Deploy models to Azure AI Foundry resources** on. 
+Azure AI Foundry portal might automatically pick up a deployment option based on your environment and configuration. We recommend using Azure AI Foundry resources for deployment whenever possible. To do that, ensure that the **Deploy models to Azure AI Foundry resources** feature is **turned on**. 
 
-:::image type="content" source="../model-inference/media/models/docs-flag-enable-foundry.gif" alt-text="An animation showing how to enable deployment to Azure AI Foundry resources (formerly known Azure AI Services)." lightbox="../model-inference/media/models/docs-flag-enable-foundry.gif":::
+:::image type="content" source="../media/concepts/deployments-overview/docs-flag-enable-foundry.png" alt-text="A screenshot showing the steps to enable deployment to Azure AI Foundry resources in the Azure AI Foundry portal." lightbox="../media/concepts/deployments-overview/docs-flag-enable-foundry.png":::
 
-Notice that once enabled, models that support multiple deployment options will default to deploy to Azure AI Foundry resources for deployment. To access other deployment options, either disable the feature or use the Azure CLI or Azure Machine Learning SDK for deployment. You can disable and enable the feature as many times as needed. Existing deployments won't be affected.
+Once the **Deploy models to Azure AI Foundry resources** feature is enabled, models that support multiple deployment options default to deploy to Azure AI Foundry resources for deployment. To access other deployment options, either disable the feature or use the Azure CLI or Azure Machine Learning SDK for deployment. You can disable and enable the feature as many times as needed without affecting existing deployments.
 
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