Merge pull request #279104 from MicrosoftDocs/main

6/24/2024 PM Publish

Author: Taojunshen

articles/ai-services/openai/concepts/content-filter.md

@@ -68,7 +68,7 @@ The default content filtering configuration for the GPT model series is set to f
 | High              | Yes| Yes | Content detected at severity levels low and medium isn't filtered. Only content at severity level high is filtered. Requires approval<sup>1</sup>.|
 | No filters | If approved<sup>1</sup>| If approved<sup>1</sup>| No content is filtered regardless of severity level detected. Requires approval<sup>1</sup>.|
 
-<sup>1</sup> For Azure OpenAI models, only customers who have been approved for modified content filtering have full content filtering control and can turn content filters off. Apply for modified content filters via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR)
+<sup>1</sup> For Azure OpenAI models, only customers who have been approved for modified content filtering have full content filtering control and can turn content filters off. Apply for modified content filters via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR) For Azure Government customers, please apply for modified content filters via this form: [Azure Government - Request Modified Content Filtering for Azure OpenAI Service](https://aka.ms/AOAIGovModifyContentFilter).
 
 This preview feature is available for the following Azure OpenAI models:
 * GPT model series (text) 

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

@@ -8,7 +8,7 @@ ms.custom:
   - ignite-2023
   - build-2024
 ms.topic: conceptual
-ms.date: 5/21/2024
+ms.date: 06/24/2024
 ms.reviewer: deeikele
 ms.author: larryfr
 author: Blackmist
@@ -18,25 +18,25 @@ author: Blackmist
 
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
-Hubs are the primary top-level Azure resource for AI studio and provide a central way for a team to govern security, connectivity, and computing resources across playgrounds and projects. Once a hub is created, it is enables developers to self-service create projects and access shared company resources without needing an IT administrator's repeated help.
+Hubs are the primary top-level Azure resource for AI studio and provide a central way for a team to govern security, connectivity, and computing resources across playgrounds and projects. Once a hub is created, developers can create projects from it and access shared company resources without needing an IT administrator's repeated help.
 
 Project workspaces that are created using a hub inherit the same security settings and shared resource access. Teams can create project workspaces as needed to organize their work, isolate data, and/or restrict access. 
 
 In this article, you learn more about hub capabilities, and how to set up a hub for your organization. You can see the resources created in the [Azure portal](https://portal.azure.com/) and in [Azure AI Studio](https://ai.azure.com).
 
 ## Rapid AI use case exploration without IT bottlenecks
 
-Successful AI applications and models typically start as prototypes, with developers testing the feasibility of an idea or assessing the quality of data or a model for a particular task. This is a steppingstone towards project funding or a full-scale implementation.
+Successful AI applications and models typically start as prototypes, where developers test the feasibility of an idea, or assess the quality of data or a model for a particular task. The prototype is a stepping stone towards project funding or a full-scale implementation.
 
-The transition from proving the feasibility of an idea to a funded project is where many organizations encounter a bottleneck in productivity, because a single platform team is responsible for the setup of cloud resources. Such a team may be the only one authorized to configure security, connectivity or other resources that may incur costs. This can cause a huge backlog, resulting in development teams getting blocked on innovating with a new idea. In Azure AI Studio, hubs help mitigate this bottleneck. IT can set up a pre-configured, reusable environment, or hub, for a team one time, and a team can use that hub to create their own projects for prototyping, building, and operating AI applications.
+When a single platform team is responsible for the setup of cloud resources, the transition from proving the feasibility of an idea to a funded project might be a bottleneck in productivity. Such a team might be the only one authorized to configure security, connectivity or other resources that might incur costs. This situation can cause a huge backlog, resulting in development teams getting blocked on innovating with a new idea. In Azure AI Studio, hubs help mitigate this bottleneck. IT can set up a preconfigured, reusable environment (a hub), for a team one time. Then the team can use that hub to create their own projects for prototyping, building, and operating AI applications.
 
 ## Set up and secure a hub for your team
 
-Get started by [creating your first hub in Azure AI Studio](../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 Studio](../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 pre-configure connectivity with these resources within a hub, so developers can access them from any new project workspace without delay on IT.
+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.
 
-[Connections](connections.md) let you access objects in AI Studio that are managed outside of your hub. For example, uploaded data on an Azure storage account, or model deployments on an existing Azure OpenAI resource. A connection can be shared with every project or made accessible to one specific project, with the option to configure key-based access or EntraID-passthrough to authorize access to users on the connected resource. Plus, as an administrator, you can track, audit, and manage connections across projects using your hub.
+[Connections](connections.md) let you access objects in AI Studio that are managed outside of your hub. For example, uploaded data on an Azure storage account, or model deployments on an existing Azure OpenAI resource. A connection can be shared with every project or made accessible to one specific project. Connections can be configured with key-based access or Microsoft Entra ID to authorize access to users on the connected resource. Plus, as an administrator, you can track, audit, and manage connections across projects using your hub.
 
 ## Shared Azure resources and configurations
 
@@ -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 AIServices resource types, which can be used to host model deployments. You can access these model deployments from connected resources in AI Studio. Keys to connected resources can be listed from the AI Studio or Azure portal. For more information, see [Find Azure AI Studio resources in the Azure portal](#find-azure-ai-studio-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 AI Studio. Keys to connected resources can be listed from the AI Studio or Azure portal. For more information, see [Find Azure AI Studio resources in the Azure portal](#find-azure-ai-studio-resources-in-the-azure-portal).
 
 ### Virtual networking
 

articles/ai-studio/concepts/architecture.md

@@ -15,14 +15,14 @@ author: Blackmist
 
 # Azure AI Studio architecture 
 
-AI Studio provides a unified experience for AI developers and data scientists to build, evaluate, and deploy AI models through a web portal, SDK, or CLI. It's built on capabilities and services provided by other Azure services.
+AI Studio provides a unified experience for AI developers and data scientists to build, evaluate, and deploy AI models through a web portal, SDK, or CLI. AI Studio is built on capabilities and services provided by other Azure services.
 
 The top level AI Studio 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.
 
 - **AI hub**: The hub is the top-level resource in AI Studio. 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, finetuning, open source and serverless model deployments.
-    - Connections to other Azure services such as Azure OpenAI, Azure AI services, and Azure AI Search. Hub-scoped connections are shared can be used by all projects.
+    - Compute resources for interactive development, finetuning, open source, and serverless model deployments.
+    - 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.
 - **AI project**: A project is a child resource of the hub. The Azure resource provider for a project is `Microsoft.MachineLearningServices/workspaces`, and the kind of resource is `Project`. The project provides the following features:
@@ -34,19 +34,25 @@ The top level AI Studio resources (hub and project) are based on Azure Machine L
 
 :::image type="content" source="../media/concepts/resource-provider-connected-resources.svg" alt-text="Diagram of the relationship between AI Studio resources." :::
 
-### Centrally setup and govern using hubs
+## Centrally set up and govern using hubs
 
 Hubs provide a central way for a team to govern security, connectivity, and computing resources across playgrounds and projects. Projects that are created using a hub inherit the same security settings and shared resource access. Teams can create as many projects as needed to organize work, isolate data, and/or restrict access.
 
-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 pre-configure connectivity with these resources within a hub, so developers can access them from any new project workspace without delay on IT.
+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.
 
-[Connections](connections.md) let you access objects in AI Studio that are managed outside of your hub. For example, uploaded data on an Azure storage account, or model deployments on an existing Azure OpenAI resource. A connection can be shared with every project or made accessible to one specific project, with the option to configure key-based access or EntraID-passthrough to authorize access to users on the connected resource. As an administrator, you can  track, audit, and manage connections across the organization from a single view in AI Studio.
+[Connections](connections.md) let you access objects in AI Studio that are managed outside of your hub. For example, uploaded data on an Azure storage account, or model deployments on an existing Azure OpenAI resource. A connection can be shared with every project or made accessible to one specific project. Connections can be configured to use key-based access or Microsoft Entra ID passthrough to authorize access to users on the connected resource. As an administrator, you can  track, audit, and manage connections across the organization from a single view in AI Studio.
 
 :::image type="content" source="../media/concepts/connected-resources-spog.png" alt-text="Screenshot of AI Studio showing an audit view of all connected resources across a hub and its projects." :::
 
+### Organize for your team's needs
+
+The number of hubs and projects you need depends on your way of working. You might create a single hub for a large team with similar data access needs. This configuration maximizes cost efficiency, resource sharing, and minimizes setup overhead. For example, a hub for all projects related to customer support.
+
+If you require isolation between dev, test, and production as part of your LLMOps or MLOps strategy, consider creating a hub for each environment. Depending on the readiness of your solution for production, you might decide to replicate your project workspaces in each environment or just in one.
+
 ## Azure resource types and providers
 
-Azure AI Studio is built on the Azure Machine Learning resource provider, and takes a dependency on a number of other Azure services. The resource providers for these services must be registered in your Azure subscription. The following table lists the resource types, provider, and kind:
+Azure AI Studio is built on the Azure Machine Learning resource provider, and takes a dependency on several other Azure services. The resource providers for these services must be registered in your Azure subscription. The following table lists the resource types, provider, and kind:
 
 [!INCLUDE [Resource provider kinds](../includes/resource-provider-kinds.md)]
 
@@ -70,9 +76,9 @@ While most of the resources used by Azure AI Studio live in your Azure subscript
     > [!NOTE]
     > If you use customer-managed keys, the metadata storage resources are created in your subscription. For more information, see [Customer-managed keys](../../ai-services/encryption/cognitive-services-encryption-keys-portal.md?context=/azure/ai-studio/context/context).
 
-Managed compute resources and managed virtual networks exist in the Microsoft subscription, but are managed by you. For example, you control which VM sizes are used for compute resources, and which outbound rules are configured for the managed virtual network.
+Managed compute resources and managed virtual networks exist in the Microsoft subscription, but you manage them. For example, you control which VM sizes are used for compute resources, and which outbound rules are configured for the managed virtual network.
 
-Managed compute resources also require vulnerability management. This is a shared responsibility between you and Microsoft. For more information, see [vulnerability management](vulnerability-management.md).
+Managed compute resources also require vulnerability management. Vulnerability management is a shared responsibility between you and Microsoft. For more information, see [vulnerability management](vulnerability-management.md).
 
 ## Role-based access control and control plane proxy
 
@@ -101,17 +107,17 @@ For more information on Azure access-based control, see [What is Azure attribute
 
 ## Containers in the storage account
 
-The default storage account for a hub has the following containers. These containers are created for each project, and the `{workspace-id}` prefix matches the unique ID for the project. The container is accessed by the project using a [connection](connections.md).
+The default storage account for a hub has the following containers. These containers are created for each project, and the `{workspace-id}` prefix matches the unique ID for the project. Projects access a container by using a [connection](connections.md).
 
 > [!TIP]
 > To find the ID for your project, go to the project in the [Azure portal](https://portal.azure.com/). Expand **Settings** and then select **Properties**. The **Workspace ID** is displayed.
 
 | Container name | Connection name | Description |
 | --- | --- | --- |
-| {workspace-ID}-azureml | workspaceartifactstore | Storage for assets such as metrics, models, and components. |
-| {workspace-ID}-blobstore| workspaceblobstore | Storage for data upload, job code snapshots, and pipeline data cache. |
-| {workspace-ID}-code | NA | Storage for notebooks, compute instances, and prompt flow. |
-| {workspace-ID}-file | NA | Alternative container for data upload. |
+| `{workspace-ID}-azureml` | workspaceartifactstore | Storage for assets such as metrics, models, and components. |
+| `{workspace-ID}-blobstore`| workspaceblobstore | Storage for data upload, job code snapshots, and pipeline data cache. |
+| `{workspace-ID}-code` | NA | Storage for notebooks, compute instances, and prompt flow. |
+| `{workspace-ID}-file` | NA | Alternative container for data upload. |
 
 ## Encryption