Merge pull request #4551 from msakande/rebrand-terminology-change-2

Rebrand terminology change 2

Author: JillGrant615

articles/ai-foundry/concepts/ai-resources.md

@@ -32,7 +32,7 @@ When a single platform team is responsible for the setup of cloud resources, the
 
 ## Set up and secure a hub for your team
 
-Get started by [creating your first hub in Azure AI Foundry portal](../how-to/create-azure-ai-resource.md), or use [Azure portal](../how-to/create-secure-ai-hub.md) or [templates](../how-to/create-azure-ai-hub-template.md) for advanced configuration options. You can customize networking, identity, encryption, monitoring, or tags, to meet compliance with your organization’s requirements.
+Get started by [creating your first hub in Azure AI Foundry portal](../how-to/create-azure-ai-resource.md), or use [Azure portal](../how-to/create-secure-ai-hub.md) or [templates](../how-to/create-azure-ai-hub-template.md) for advanced configuration options. You can customize networking, identity, encryption, monitoring, or tags, to meet compliance with your organization's requirements.
 
 Often, projects in a business domain require access to the same company resources such as vector indices, model endpoints, or repos. As a team lead, you can preconfigure connectivity with these resources within a hub, so developers can access them from any new project workspace without delay on IT.
 
@@ -76,7 +76,7 @@ Projects also have specific settings that only hold for that project:
 
 ## Azure AI services API access keys
 
-The hub allows you to set up connections to existing Azure OpenAI or Azure AI Services resource types, which can be used to host model deployments. You can access these model deployments from connected resources in Azure AI Foundry portal. Keys to connected resources can be listed from the Azure AI Foundry portal or Azure portal. For more information, see [Find Azure AI Foundry resources in the Azure portal](#find-azure-ai-foundry-resources-in-the-azure-portal).
+The hub allows you to set up connections to existing Azure OpenAI or Azure AI Services resource types, which can be used to host model deployments. You can access these model deployments from connected resources in Azure AI Foundry portal. Keys to connected resources can be listed from the Azure AI Foundry portal or Azure portal. For more information, see [Find Azure AI Foundry Service in the Azure portal](#find-azure-ai-foundry-services-in-the-azure-portal).
 
 ### Virtual networking
 
@@ -115,7 +115,7 @@ If you require to group costs of these different services together, we recommend
 
 You can use [cost management](/azure/cost-management-billing/costs/quick-acm-cost-analysis) and [Azure resource tags](/azure/azure-resource-manager/management/tag-resources) to help with a detailed resource-level cost breakdown, or run [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator/) on the above listed resources to obtain a pricing estimate. For more information, see [Plan and manage costs for Azure AI services](../how-to/costs-plan-manage.md).
 
-## Find Azure AI Foundry resources in the Azure portal
+## Find Azure AI Foundry Services in the Azure portal
 
 In the Azure portal, you can find resources that correspond to your project in Azure AI Foundry portal.
 

articles/ai-foundry/concepts/architecture.md

@@ -22,20 +22,18 @@ Azure AI Foundry provides a unified experience for AI developers and data scient
 
 At the top level, Azure AI Foundry provides access to the following resources:
 
-<!-- The top level Azure AI Foundry resources (hub and project) are based on Azure Machine Learning. Connected resources, such as Azure OpenAI, Azure AI services, and Azure AI Search, are used by the hub and project in reference, but follow their own resource management lifecycle. -->
-
 - **Azure OpenAI**: Provides access to the latest OpenAI models. You can create secure deployments, try playgrounds, fine tune models, content filters, and batch jobs. The Azure OpenAI resource provider is `Microsoft.CognitiveServices/account` and the kind of resource is `OpenAI`. You can also connect to Azure OpenAI by using a kind of `AIServices`, which also includes other [Azure AI services](/azure/ai-services/what-are-ai-services).
 
     When you use Azure AI Foundry portal, you can directly work with Azure OpenAI without an Azure Studio project. Or you can use Azure OpenAI through a project.
 
     For more information, visit [Azure OpenAI in Azure AI Foundry portal](../azure-openai-in-azure-ai-foundry.md).
 
-- **Management center**: The management center streamlines governance and management of Azure AI Foundry resources such as hubs, projects, connected resources, and deployments.
+- **Management center**: The management center streamlines governance and management of Azure AI Foundry services such as hubs, projects, connected resources, and deployments.
 
     For more information, visit [Management center](management-center.md).
 - **Azure AI Foundry hub**: The hub is the top-level resource in Azure AI Foundry portal, and is based on the Azure Machine Learning service. The Azure resource provider for a hub is `Microsoft.MachineLearningServices/workspaces`, and the kind of resource is `Hub`. It provides the following features:
     - Security configuration including a managed network that spans projects and model endpoints.
-    - Compute resources for interactive development, fine-tuning, open source, and serverless model deployments.
+    - Compute resources for interactive development, fine-tuning, open source, and standard deployment for models.
     - Connections to other Azure services such as Azure OpenAI, Azure AI services, and Azure AI Search. Hub-scoped connections are shared with projects created from the hub.
     - Project management. A hub can have multiple child projects.
     - An associated Azure storage account for data upload and artifact storage.
@@ -48,7 +46,7 @@ At the top level, Azure AI Foundry provides access to the following resources:
     - Project-scoped connections. For example, project members might need private access to data stored in an Azure Storage account without giving that same access to other projects.
     - Open source model deployments from catalog and fine-tuned model endpoints.
 
-    :::image type="content" source="../media/concepts/resource-provider-connected-resources.svg" alt-text="Diagram of the relationship between Azure AI Foundry resources." :::
+    :::image type="content" source="../media/concepts/resource-provider-connected-resources.svg" alt-text="Diagram of the relationship between Azure AI Foundry services." :::
 
     For more information, visit [Hubs and projects overview](ai-resources.md).
 

articles/ai-foundry/concepts/content-filtering.md

@@ -26,7 +26,7 @@ author: PatrickFarley
 
 The content filtering system is powered by [Azure AI Content Safety](../../ai-services/content-safety/overview.md), and it works by running both the prompt input and completion output through a set of classification models designed to detect and prevent the output of harmful content. Variations in API configurations and application design might affect completions and thus filtering behavior.
 
-With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later on). Models available through **serverless APIs** have content filtering enabled by default. To learn more about the default content filter enabled for serverless APIs, see [Guardrails & controls for models curated by Azure AI in the model catalog](model-catalog-content-safety.md).
+With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later on). Models available through **serverless APIs** have content filtering enabled by default. To learn more about the default content filter enabled for serverless APIs, see [Guardrails & controls for Azure Direct Models in the model catalog](model-catalog-content-safety.md).
 
 ## Language support
 

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

@@ -25,7 +25,7 @@ Deployment options vary depending on the model offering:
 
 Azure AI Foundry offers four different deployment options:
 
-|Name                           | Azure OpenAI | Azure AI model inference | Standard deployment | Managed compute |
+|Name                           | Azure OpenAI | Azure AI Foundry Models | Standard deployment | Managed compute |
 |-------------------------------|----------------------|-------------------|----------------|-----------------|
 | Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Standard deployment](../../ai-foundry/model-inference/concepts/models.md) | [Standard deployment](../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 services resource | AI project resource | AI project resource |
@@ -37,7 +37,7 @@ Azure AI Foundry offers four different deployment options:
 | Key-less authentication       | Yes | Yes | No  | No  |
 | Best suited when              | You're planning to use only OpenAI models | You're planning to take advantage of the flagship models in Azure AI catalog, including OpenAI. | You're planning to use a single model from a specific provider (excluding OpenAI). | If you plan to use open models and you have enough compute quota available in your subscription. |
 | Billing bases                 | Token usage & [provisioned throughput units](../../ai-services/openai/concepts/provisioned-throughput.md)        | Token usage       | Token usage<sup>1</sup>      | Compute core hours<sup>2</sup> |
-| Deployment instructions       | [Deploy to Azure OpenAI](../how-to/deploy-models-openai.md) | [Deploy to Azure AI model inference](../model-inference/how-to/create-model-deployments.md) | [Deploy to Standard deployment](../how-to/deploy-models-serverless.md) | [Deploy to Managed compute](../how-to/deploy-models-managed.md) |
+| Deployment instructions       | [Deploy to Azure OpenAI](../how-to/deploy-models-openai.md) | [Deploy to Foundry Models](../model-inference/how-to/create-model-deployments.md) | [Deploy to Standard deployment](../how-to/deploy-models-serverless.md) | [Deploy to Managed compute](../how-to/deploy-models-managed.md) |
 
 <sup>1</sup> A minimal endpoint infrastructure is billed per minute. You aren't billed for the infrastructure that hosts the model in pay-as-you-go. After you delete the endpoint, no further charges accrue.
 
@@ -50,7 +50,7 @@ Azure AI Foundry offers four different deployment options:
 
 Azure AI Foundry encourages you to explore various deployment options and choose the one that best suites your business and technical needs. In general, Consider using the following approach to select a deployment option:
 
-* Start with [Azure AI model inference](../../ai-foundry/model-inference/overview.md), which is the option with the largest scope. This option allows you to iterate and prototype faster in your application without having to rebuild your architecture each time you decide to change something. If you're using Azure AI Foundry hubs or projects, enable this option by [turning on the Azure AI model inference feature](../model-inference/how-to/quickstart-ai-project.md#configure-the-project-to-use-azure-ai-model-inference).
+* Start with [Foundry Models](../../ai-foundry/model-inference/overview.md), which is the option with the largest scope. This option allows you to iterate and prototype faster in your application without having to rebuild your architecture each time you decide to change something. If you're using Azure AI Foundry hubs or projects, enable this option by [turning on the Foundry Models feature](../model-inference/how-to/quickstart-ai-project.md#configure-the-project-to-use-foundry-models).
 
 * When you're looking to use a specific model:
 
@@ -63,8 +63,8 @@ Azure AI Foundry encourages you to explore various deployment options and choose
 
 ## Related content
 
-* [Configure your AI project to use Azure AI model inference](../../ai-foundry/model-inference/how-to/quickstart-ai-project.md)
-* [Add and configure models to Azure AI model inference](../model-inference/how-to/create-model-deployments.md)
+* [Configure your AI project to use Foundry Models](../../ai-foundry/model-inference/how-to/quickstart-ai-project.md)
+* [Add and configure models to Foundry Models](../model-inference/how-to/create-model-deployments.md)
 * [Deploy Azure OpenAI models with Azure AI Foundry](../how-to/deploy-models-openai.md)
 * [Deploy open models with Azure AI Foundry](../how-to/deploy-models-managed.md)
 * [Model catalog and collections in Azure AI Foundry portal](../how-to/model-catalog-overview.md)

articles/ai-foundry/concepts/model-catalog-content-safety.md

@@ -1,7 +1,7 @@
 ---
-title: Guardrails & controls for models curated by Azure AI in the model catalog
+title: Guardrails & controls for Azure Direct Models
 titleSuffix: Azure AI Foundry
-description: Learn about Guardrails & controls for models deployed using serverless APIs, using Azure AI Foundry.
+description: Learn about Guardrails & controls for models deployed via standard deployment.
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: conceptual
@@ -13,7 +13,7 @@ reviewer: ositanachi
 ms.custom: 
 ---
 
-# Guardrails & controls for models curated by Azure AI in the model catalog
+# Guardrails & controls for Azure Direct Models
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
@@ -24,7 +24,7 @@ In this article, learn about Guardrails & controls capabilities for models from
 
 Azure AI uses a default configuration of [Azure AI Content Safety](/azure/ai-services/content-safety/overview) content filters to detect harmful content across four categories including hate and fairness, self-harm, sexual, and violence for models deployed via serverless APIs. To learn more about content filtering, see [Understand harm categories](#understand-harm-categories).
 
-The default content filtering configuration for text models is set to filter at the medium severity threshold, filtering any detected content at this level or higher. For image models, the default content filtering configuration is set at the low configuration threshold, filtering at this level or higher. For models deployed using the [Azure AI model inference service](../../ai-foundry/model-inference/how-to/configure-content-filters.md), you can create configurable filters by selecting the **Content filters** tab within the **Guardrails & controls** page of the Azure AI Foundry portal.
+The default content filtering configuration for text models is set to filter at the medium severity threshold, filtering any detected content at this level or higher. For image models, the default content filtering configuration is set at the low configuration threshold, filtering at this level or higher. For models deployed using the [Azure AI Foundry Models](../../ai-foundry/model-inference/how-to/configure-content-filters.md), you can create configurable filters by selecting the **Content filters** tab within the **Guardrails & controls** page of the Azure AI Foundry portal.
 
 > [!TIP]
 > Content filtering isn't available for certain model types that are deployed via serverless APIs. These model types include embedding models and time series models.
@@ -34,7 +34,7 @@ Content filtering occurs synchronously as the service processes prompts to gener
 - When you first deploy a language model
 - Later, by selecting the content filtering toggle on the deployment details page
 
-Suppose you decide to use an API other than the [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api) to work with a model that is deployed via a serverless API. In such a situation, content filtering isn't enabled unless you implement it separately by using Azure AI Content Safety. To get started with Azure AI Content Safety, see [Quickstart: Analyze text content](/azure/ai-services/content-safety/quickstart-text). You run a higher risk of exposing users to harmful content if you don't use content filtering when working with models that are deployed via serverless APIs.
+Suppose you decide to use an API other than the [Azure AI Foundry Models API](/azure/ai-studio/reference/reference-model-inference-api) to work with a model that is deployed via a serverless API. In such a situation, content filtering isn't enabled unless you implement it separately by using Azure AI Content Safety. To get started with Azure AI Content Safety, see [Quickstart: Analyze text content](/azure/ai-services/content-safety/quickstart-text). You run a higher risk of exposing users to harmful content if you don't use content filtering when working with models that are deployed via serverless APIs.
 
 [!INCLUDE [content-safety-harm-categories](../includes/content-safety-harm-categories.md)]
 

articles/ai-foundry/concepts/model-lifecycle-retirement.md

@@ -19,7 +19,7 @@ reviewer: fkriti
 Azure AI Foundry Models in the model catalog are continually refreshed with newer and more capable models. As part of this process, model providers might deprecate and retire their older models, and you might need to update your applications to use a newer model. This document communicates information about the model lifecycle and deprecation timelines and explains how you're informed of model lifecycle stages.
 
 > [!IMPORTANT]
-> This article describes deprecation and retirement only for Azure Direct models and Azure Ecosystem models models in Foundry Models. For information about deprecation and retirement for Azure OpenAI in Foundry Models, see the Azure OpenAI models lifecycle documentation.
+> This article describes deprecation and retirement only for Azure Direct models and Azure Ecosystem models models in Foundry Models. For information about deprecation and retirement for Azure OpenAI in Foundry Models, see the [Azure OpenAI models lifecycle](../../ai-services/openai/concepts/model-retirements.md?context=/azure/ai-foundry/context/context) documentation.
 
 ## Model lifecycle stages
 
@@ -58,7 +58,7 @@ Models labeled _Retired_ are no longer available for use. You can't create new d
 
 - Models are labeled _Deprecated_ and remain in the deprecated state for at least 90 days before being moved to the retired state. During this notification period, you can migrate any existing deployments to newer or replacement models.
 
-- For each subscription that has a model deployed as a severless API or deployed to the Azure AI model inference, members of the _owner_, _contributor_, _reader_, monitoring contributor_, and _monitoring reader_ roles receive a notification when a model deprecation is announced. The notification contains the dates when the model enters legacy, deprecated, and retired states. The notification might provide information about possible replacement model options, if applicable.
+- For each subscription that has a model deployed as a standard deployment or deployed in Foundry Models, members of the _owner_, _contributor_, _reader_, monitoring contributor_, and _monitoring reader_ roles receive a notification when a model deprecation is announced. The notification contains the dates when the model enters legacy, deprecated, and retired states. The notification might provide information about possible replacement model options, if applicable.
 
 The following tables list the timelines for models that are on track for retirement. The specified dates are in UTC time.