Merge branch 'release-build-ai-foundry-non-FDP-features' into patch-maas-rebrand

Author: ssalgadodev

articles/ai-foundry/concepts/ai-red-teaming-agent.md

@@ -17,11 +17,11 @@ author: lgayhardt
 
 The AI Red Teaming Agent (preview) is a powerful tool designed to help organizations proactively find safety risks associated with generative AI systems during design and development of generative AI models and applications.
 
-Traditional red teaming involves exploiting the cyber kill chain and describes the process by which a system is tested for security vulnerabilities. However, with the rise of generative AI, the term AI red teaming has been coined to describe probing for novel risks (both content safety and security related) that these systems present and refers to simulating the behavior of an adversarial user who is trying to cause your AI system to misbehave in a particular way.
+Traditional red teaming involves exploiting the cyber kill chain and describes the process by which a system is tested for security vulnerabilities. However, with the rise of generative AI, the term AI red teaming has been coined to describe probing for novel risks (both content and security related) that these systems present and refers to simulating the behavior of an adversarial user who is trying to cause your AI system to misbehave in a particular way.
 
 The AI Red Teaming Agent leverages Microsoft's open-source framework for Python Risk Identification Tool's ([PyRIT](https://github.com/Azure/PyRIT)) AI red teaming capabilities along with Azure AI Foundry's [Risk and Safety Evaluations](./evaluation-metrics-built-in.md#risk-and-safety-evaluators) to help you automatically assess safety issues in three ways:
 
-- **Automated scans for content safety risks:** Firstly, you can automatically scan your model and application endpoints for safety risks by simulating adversarial probing.
+- **Automated scans for content risks:** Firstly, you can automatically scan your model and application endpoints for safety risks by simulating adversarial probing.
 - **Evaluate probing success:** Next, you can evaluate and score each attack-response pair to generate insightful metrics such as Attack Success Rate (ASR).
 - **Reporting and logging** Finally, you can generate a score card of the attack probing techniques and risk categories to help you decide if the system is ready for deployment. Findings can be logged, monitored, and tracked over time directly in Azure AI Foundry, ensuring compliance and continuous risk mitigation.
 

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/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#content-safety-for-models-deployed-via-standard-deployments) | [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 standard deployment. 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/evaluation-metrics-built-in.md

@@ -197,12 +197,12 @@ We support evaluating vulnerability towards the following types of jailbreak att
 - **Direct attack jailbreak** (also known as UPIA or User Prompt Injected Attack) injects prompts in the user role turn of conversations or queries to generative AI applications. Jailbreaks are when a model response bypasses the restrictions placed on it. Jailbreak also happens where an LLM deviates from the intended task or topic.
 - **Indirect attack jailbreak** (also known as XPIA or cross domain prompt injected attack) injects prompts in the returned documents or context of the user's query to generative AI applications.
 
-*Evaluating direct attack* is a comparative measurement using the content safety evaluators as a control. It isn't its own AI-assisted evaluator. Run `ContentSafetyEvaluator` on two different, red-teamed datasets:
+*Evaluating direct attack* is a comparative measurement using the Azure AI Content Safety evaluators as a control. It isn't its own AI-assisted evaluator. Run `ContentSafetyEvaluator` on two different, red-teamed datasets:
 
 - Baseline adversarial test dataset.
 - Adversarial test dataset with direct attack jailbreak injections in the first turn.
 
-You can do this with functionality and attack datasets generated with the [direct attack simulator](../how-to/develop/simulator-interaction-data.md#simulating-jailbreak-attacks) with the same randomization seed. Then you can evaluate jailbreak vulnerability by comparing results from content safety evaluators between the two test dataset's aggregate scores for each safety evaluator. A direct attack jailbreak defect is detected when there's presence of content harm response detected in the second direct attack injected dataset when there was none or lower severity detected in the first control dataset.
+You can do this with functionality and attack datasets generated with the [direct attack simulator](../how-to/develop/simulator-interaction-data.md#simulating-jailbreak-attacks) with the same randomization seed. Then you can evaluate jailbreak vulnerability by comparing results from Azure AI Content Safety evaluators between the two test dataset's aggregate scores for each safety evaluator. A direct attack jailbreak defect is detected when there's presence of content harm response detected in the second direct attack injected dataset when there was none or lower severity detected in the first control dataset.
 
 ### Indirect attack definition and label