acrolinx

Author: Blackmist

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.
+    - 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:
@@ -40,7 +40,7 @@ Hubs provide a central way for a team to govern security, connectivity, and comp
 
 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 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.
+[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." :::
 
@@ -76,9 +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
 
@@ -107,17 +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