Merge pull request #275717 from eric-urban/eur/build-ai-studio-follow-6

UI refresh

Author: v-dirichards

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

@@ -81,9 +81,9 @@ While projects show up as their own tracking resources in the Azure portal, they
 
 ## Connections to Azure and third-party resources
 
-Azure AI offers a set of connectors that allows you to connect to different types of data sources and other Azure tools. You can take advantage of connectors to connect with data such as indices in Azure AI Search to augment your flows.
+Azure AI offers a set of connectors that allows you to connect to different types of data sources and other Azure tools. You can take advantage of connectors to connect with data such as indexes in Azure AI Search to augment your flows.
 
-Connections can be set up as shared with all projects in the same hub, or created exclusively for one project. To manage project connections via Azure AI Studio, navigate to a project page, then navigate to **Project settings** > **Connections**. To manage shared connections, navigate to the **Manage** page. As an administrator, you can audit both shared and project-scoped connections on a hub level to have a single pane of glass of connectivity across projects.
+Connections can be set up as shared with all projects in the same hub, or created exclusively for one project. To manage project connections via Azure AI Studio, go to your project and then select **Settings** > **Connections**. To manage shared connections for a hub, go to your hub settings. As an administrator, you can audit both shared and project-scoped connections on a hub level to have a single pane of glass of connectivity across projects.
 
 ## Azure AI dependencies
 
@@ -113,9 +113,9 @@ In the Azure portal, you can find resources that correspond to your project in A
 > This section assumes that the hub and project are in the same resource group. 
 1. In [Azure AI Studio](https://ai.azure.com), go to a project and select **Settings** to view your project resources such as connections and API keys. There's a link to your hub in Azure AI Studio and links to view the corresponding project resources in the [Azure portal](https://portal.azure.com).
 
-    :::image type="content" source="../media/concepts/azureai-project-view-ai-studio.png" alt-text="Screenshot of the project and related resources in the Azure AI Studio." lightbox="../media/concepts/azureai-project-view-ai-studio.png":::
+    :::image type="content" source="../media/concepts/azureai-project-view-ai-studio.png" alt-text="Screenshot of the AI Studio project overview page with links to the Azure portal." lightbox="../media/concepts/azureai-project-view-ai-studio.png":::
 
-1. Select **Manage in the Azure Portal** to see your hub in the Azure portal. 
+1. Select **Manage in Azure Portal** to see your hub in the [Azure portal](https://portal.azure.com). 
 
 ## Next steps
 

articles/ai-studio/concepts/architecture.md

@@ -75,15 +75,15 @@ For more information, see [Role-based access control in Azure AI Studio](rbac-ai
 
 ## Attribute-based access control
 
-Each AI hub you create has a default storage account. Each child AI project of the AI hub inherits the storage account of the AI hub. The storage account is used to store data and artifacts.
+Each hub you create has a default storage account. Each child project of the hub inherits the storage account of the hub. The storage account is used to store data and artifacts.
 
-To secure the shared storage account, Azure AI Studio uses both Azure RBAC and Azure attribute-based access control (Azure ABAC). Azure ABAC is a security model that defines access control based on attributes associated with the user, resource, and environment. Each AI project has:
+To secure the shared storage account, Azure AI Studio uses both Azure RBAC and Azure attribute-based access control (Azure ABAC). Azure ABAC is a security model that defines access control based on attributes associated with the user, resource, and environment. Each project has:
 
 - A service principal that is assigned the Storage Blob Data Contributor role on the storage account.
 - A unique ID (workspace ID).
-- A set of containers in the storage account. Each container has a prefix that corresponds to the workspace ID value for the AI project.
+- A set of containers in the storage account. Each container has a prefix that corresponds to the workspace ID value for the project.
 
-The role assignment for each AI project's service principal has a condition that only allows the service principal access to containers with the matching prefix value. This condition ensures that each AI project can only access its own containers.
+The role assignment for each project's service principal has a condition that only allows the service principal access to containers with the matching prefix value. This condition ensures that each project can only access its own containers.
 
 > [!NOTE]
 > For data encryption in the storage account, the scope is the entire storage and not per-container. So all containers are encrypted using the same key (provided either by Microsoft or by the customer).
@@ -92,10 +92,10 @@ For more information on Azure access-based control, see [What is Azure attribute
 
 ## Containers in the storage account
 
-The default storage account for an AI hub has the following containers. These containers are created for each AI project, and the `{workspace-id}` prefix matches the unique ID for the AI project. The container is accessed by the AI 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. The container is accessed by the project using a [connection](connections.md).
 
 > [!TIP]
-> To find the ID for your AI project, go to the AI project in the [Azure portal](https://portal.azure.com/). Expand **Settings** and then select **Properties**. The **Workspace ID** is displayed.
+> 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 |
 | --- | --- | --- |

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

@@ -153,7 +153,7 @@ If the built-in roles are insufficient, you can create custom roles. Custom role
 
 When configuring a hub to use a customer-managed key (CMK), an Azure Key Vault is used to store the key. The user or service principal used to create the workspace must have owner or contributor access to the key vault.
 
-If your Azure AI hub is configured with a **user-assigned managed identity**, the identity must be granted the following roles. These roles allow the managed identity to create the Azure Storage, Azure Cosmos DB, and Azure Search resources used when using a customer-managed key:
+If your AI Studio hub is configured with a **user-assigned managed identity**, the identity must be granted the following roles. These roles allow the managed identity to create the Azure Storage, Azure Cosmos DB, and Azure Search resources used when using a customer-managed key:
 
 - `Microsoft.Storage/storageAccounts/write`
 - `Microsoft.Search/searchServices/write`
@@ -175,7 +175,7 @@ An Azure Container Registry instance is an optional dependency for Azure AI Stud
 | AI Studio hub system-assigned managed identity | ✓ | ✓ |
 | AI Studio hub user-assigned managed identity </br>with the **ACRPull** role assigned to the identity |  | ✓ |
 
-A system-assigned managed identity is automatically assigned to the correct roles when the Azure AI hub is created. If you're using a user-assigned managed identity, you must assign the **ACRPull** role to the identity.
+A system-assigned managed identity is automatically assigned to the correct roles when the hub is created. If you're using a user-assigned managed identity, you must assign the **ACRPull** role to the identity.
 
 ## Scenario: Use Azure Application Insights for logging
 

articles/ai-studio/concepts/safety-evaluations-transparency-note.md

@@ -41,7 +41,7 @@ The Azure AI Studio safety evaluations let users evaluate the output of their ge
 
 ### System behavior
 
-Azure AI Studio provisions an Azure Open AI GPT-4 model and orchestrates adversarial attacks against your application to generate a high quality test dataset. It then provisions another GPT-4 model to annotate your test dataset for content and security. Users provide their generative AI application endpoint that they wish to test, and the safety evaluations will output a static test dataset against that endpoint along with its content risk label (Very low, Low, Medium, High) and reasoning for the AI-generated label.
+Azure AI Studio provisions an Azure OpenAI GPT-4 model and orchestrates adversarial attacks against your application to generate a high quality test dataset. It then provisions another GPT-4 model to annotate your test dataset for content and security. Users provide their generative AI application endpoint that they wish to test, and the safety evaluations will output a static test dataset against that endpoint along with its content risk label (Very low, Low, Medium, High) and reasoning for the AI-generated label.
 
 ### Use cases
 

articles/ai-studio/how-to/configure-managed-network.md

@@ -152,7 +152,7 @@ Before following the steps in this article, make sure you have the following pre
 
 ### Connectivity to other services
 
-* Azure AI services provisioned with Azure AI hub and Azure AI Search attached with Azure AI hub should be public.
+* Azure AI services provisioned with Azure AI Studio hub and Azure AI Search attached should be public.
 * The "Add your data" feature in the Azure AI Studio playground doesn't support using a virtual network or private endpoint on the following resources:
     * Azure AI Search
     * Azure OpenAI

articles/ai-studio/how-to/connections-add.md

@@ -69,11 +69,11 @@ If your hub is configured for [network isolation](configure-managed-network.md),
 
 To create an outbound private endpoint rule to the data source, use the following steps:
 
-1. Sign in to the [Azure portal](https://portal.azure.com), and select the Azure AI hub.
+1. Sign in to the [Azure portal](https://portal.azure.com), and select the Azure AI Studio hub.
 1. Select **Networking**, then **Workspace managed outbound access**.
 1. To add an outbound rule, select **Add user-defined outbound rules**. From the **Workspace outbound rules** sidebar, provide the following information:
 
-    - **Rule name**: A name for the rule. The name must be unique for the AI hub.
+    - **Rule name**: A name for the rule. The name must be unique for the AI Studio hub.
     - **Destination type**: Private Endpoint.
     - **Subscription**: The subscription that contains the Azure resource you want to connect to.
     - **Resource type**: `Microsoft.Storage/storageAccounts`. This resource provider is used for Azure Storage, Azure Data Lake Storage Gen2, and Microsoft OneLake.

articles/ai-studio/how-to/data-add.md

@@ -66,18 +66,18 @@ These steps explain how to create a File typed data in Azure AI Studio:
 
 1. Choose your **Data source**. You have three options to choose a data source.
    - You can select data from **Existing Connections**.
-   - You can **Get data with Storage URL** if you have a direct URL to a storage account or a public accessible HTTPS server.
-   - You can choose **Upload files/folders** to upload a folder from your local drive.
+   - You can select **Get data with Storage URL** if you have a direct URL to a storage account or a public accessible HTTPS server.
+   - You can select **Upload files/folders** to upload a folder from your local drive.
 
     :::image type="content" source="../media/data-add/select-connection.png" alt-text="This screenshot shows the existing connections.":::
 
-    1. **Existing Connections**: You can select an existing connection, browse into this connection, and choose a file you need. If the existing connections don't work for you, select the **New connection** button at the upper right.
+    - **Existing Connections**: You can select an existing connection, browse into this connection, and choose a file you need. If the existing connections don't work for you, select the **New connection** button at the upper right.
     :::image type="content" source="../media/data-add/new-connection.png" alt-text="This screenshot shows the creation of a new connection to an external asset.":::
 
-    1. **Get data with Storage URL**: You can choose the **Type** as "File", and then provide a URL based on the supported URL formats listed on that page.
+    - **Get data with Storage URL**: You can choose the **Type** as "File", and then provide a URL based on the supported URL formats listed on that page.
     :::image type="content" source="../media/data-add/file-url.png" alt-text="This screenshot shows provision of a URL that points to a file.":::
 
-    1. **Upload files/folders**: You can select **Upload files or folder**, select **Upload files**, and choose the local file to upload. The file uploads into the default "workspaceblobstore" connection.
+    - **Upload files/folders**: You can select **Upload files or folder**, select **Upload files**, and choose the local file to upload. The file uploads into the default "workspaceblobstore" connection.
     :::image type="content" source="../media/data-add/upload.png" alt-text="This screenshot shows the step to upload files/folders.":::
 
     1. Select **Next** after you choose the data source.
@@ -103,15 +103,15 @@ A Folder (`uri_folder`) data source type points to a *folder* on a storage resou
 
        :::image type="content" source="../media/data-add/select-connection.png" alt-text="This screenshot shows the existing connections.":::
 
-    1. **Existing Connections**: You can select an existing connection and browse into this connection and choose a file you need. If the existing connections don't work for you, you can select the **New connection** button at the right.
+    - **Existing Connections**: You can select an existing connection and browse into this connection and choose a file you need. If the existing connections don't work for you, you can select the **New connection** button at the right.
 
        :::image type="content" source="../media/data-add/choose-folder.png" alt-text="This screenshot shows the step to choose a folder from an existing connection.":::
 
-    1. **Get data with Storage URL**: You can choose the **Type** as "Folder", and provide a URL based on the supported URL formats listed on that page.
+    - **Get data with Storage URL**: You can choose the **Type** as "Folder", and provide a URL based on the supported URL formats listed on that page.
 
        :::image type="content" source="../media/data-add/folder-url.png" alt-text="This screenshot shows the step to provide a URL that points to a folder.":::
 
-    1. **Upload files/folders**: You can select **Upload files or folder**, and select **Upload files**, and choose the local file to upload. The file resources upload into the default "workspaceblobstore" connection.
+    - **Upload files/folders**: You can select **Upload files or folder**, and select **Upload files**, and choose the local file to upload. The file resources upload into the default "workspaceblobstore" connection.
 
        :::image type="content" source="../media/data-add/upload.png" alt-text="This screenshot shows the step to upload files/folders.":::
 
@@ -126,20 +126,17 @@ A Folder (`uri_folder`) data source type points to a *folder* on a storage resou
 ### Delete data
 
 > [!IMPORTANT]
-> ***By design*, data deletion is not supported.**
->
-> If Azure AI allowed data deletion, it would have the following adverse effects:
->
-> - **Production jobs** that consume data that is later deleted would fail.
-> - ML experiment reproduction would become more difficult.
-> - Job **lineage** would break, because it would become impossible to view the deleted data version.
-> - You could no longer **track and audit** correctly, since versions could be missing.
->
-> Therefore, data *immutability* provides a level of protection when working in a team that creates production workloads.
+> Data deletion is not supported. Data is immutable in AI Studio. Once you create a data version, it can't be modified or deleted. This immutability provides a level of protection when working in a team that creates production workloads.
+
+If AI Studio allowed data deletion, it would have the following adverse effects:
+- Production jobs that consume data that is later deleted would fail.
+- Machine learning experiment reproduction would become more difficult.
+- Job lineage would break, because it would become impossible to view the deleted data version.
+- You could no longer track and audit correctly, since versions could be missing.
 
 When a data resource is erroneously created - for example, with an incorrect name, type or path - Azure AI offers solutions to handle the situation without the negative consequences of deletion:
 
-|*I want to delete this data because...* | Solution  |
+|Reason that you might want to delete data | Solution  |
 |---------|---------|
 |The **name** is incorrect     |  [Archive the data](#archive-data)       |
 |The team **no longer uses** the data | [Archive the data](#archive-data) |

articles/ai-studio/how-to/deploy-jais-models.md

@@ -28,9 +28,9 @@ You can find the JAIS model in the [Model Catalog](model-catalog.md) by filterin
 - An [Azure AI Studio hub](../how-to/create-azure-ai-resource.md).
 
     > [!IMPORTANT]
-    > For JAIS models, the serverless API model deployment offering is only available with AI hubs created in East US 2 or Sweden Central region.
+    > For JAIS models, the serverless API model deployment offering is only available with hubs created in East US 2 or Sweden Central region.
 
-- An [Azure AI project](../how-to/create-projects.md) in Azure AI Studio.
+- An [AI Studio project](../how-to/create-projects.md) in Azure AI Studio.
 - Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, see [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).
 
 

articles/ai-studio/how-to/deploy-models-cohere-command.md

@@ -55,7 +55,7 @@ The previously mentioned Cohere models can be deployed as a service with pay-as-
     > [!IMPORTANT]
     > For Cohere family models, the serverless API model deployment offering is only available with hubs created in **EastUS2** or **Sweden Central** region.
 
-- An [Azure AI project](../how-to/create-projects.md) in Azure AI Studio.
+- An [AI Studio project](../how-to/create-projects.md) in Azure AI Studio.
 - Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, see [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).
 
 

articles/ai-studio/how-to/deploy-models-cohere-embed.md

@@ -50,12 +50,12 @@ The previously mentioned Cohere models can be deployed as a service with pay-as-
 ### Prerequisites
 
 - An Azure subscription with a valid payment method. Free or trial Azure subscriptions won't work. If you don't have an Azure subscription, create a [paid Azure account](https://azure.microsoft.com/pricing/purchase-options/pay-as-you-go) to begin.
-- An [Azure AI hub resource](../how-to/create-azure-ai-resource.md).
+- An [AI Studio hub](../how-to/create-azure-ai-resource.md).
 
     > [!IMPORTANT]
-    > For Cohere family models, the serverless API model deployment offering is only available with AI hubs created in **EastUS2** or **Sweden Central** region.
+    > For Cohere family models, the serverless API model deployment offering is only available with hubs created in **EastUS2** or **Sweden Central** region.
 
-- An [Azure AI project](../how-to/create-projects.md) in Azure AI Studio.
+- An [AI Studio project](../how-to/create-projects.md) in Azure AI Studio.
 - Azure role-based access controls are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, see [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).