hub and project terminology

eric-urban · eric-urban · commit 8e1f2d91d4ed · 2024-05-20T05:44:15.000-07:00
diff --git a/articles/ai-studio/concepts/architecture.md b/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 an 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 |
 | --- | --- | --- |
diff --git a/articles/ai-studio/concepts/rbac-ai-studio.md b/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
 
diff --git a/articles/ai-studio/how-to/configure-managed-network.md b/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
diff --git a/articles/ai-studio/how-to/connections-add.md b/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.
diff --git a/articles/ai-studio/how-to/deploy-jais-models.md b/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).
 
 
diff --git a/articles/ai-studio/how-to/deploy-models-cohere-command.md b/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).
 
 
diff --git a/articles/ai-studio/how-to/deploy-models-cohere-embed.md b/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).
 
 
diff --git a/articles/ai-studio/how-to/deploy-models-llama.md b/articles/ai-studio/how-to/deploy-models-llama.md
@@ -62,12 +62,12 @@ If you need to deploy a different model, [deploy it to managed compute](#deploy-
 - An [AI Studio hub](../how-to/create-azure-ai-resource.md).
 
     > [!IMPORTANT]
-    > For Meta Llama 3 models, the pay-as-you-go model deployment offering is only available with AI hubs created in **East US 2** and **Sweden Central** regions.
+    > For Meta Llama 3 models, the pay-as-you-go model deployment offering is only available with hubs created in **East US 2** and **Sweden Central** regions.
 
 - 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 __owner__ or __contributor__ role for the Azure subscription. Alternatively, your account can be assigned a custom role that has the following permissions:
 
-    - On the Azure subscription—to subscribe the Azure AI project to the Azure Marketplace offering, once for each project, per offering:
+    - On the Azure subscription—to subscribe the AI Studio project to the Azure Marketplace offering, once for each project, per offering:
       - `Microsoft.MarketplaceOrdering/agreements/offers/plans/read`
       - `Microsoft.MarketplaceOrdering/agreements/offers/plans/sign/action`
       - `Microsoft.MarketplaceOrdering/offerTypes/publishers/offers/plans/agreements/read`
@@ -78,7 +78,7 @@ If you need to deploy a different model, [deploy it to managed compute](#deploy-
       - `Microsoft.SaaS/resources/read`
       - `Microsoft.SaaS/resources/write`
  
-    - On the Azure AI project—to deploy endpoints (the Azure AI Developer role contains these permissions already):
+    - On the AI Studio project—to deploy endpoints (the Azure AI Developer role contains these permissions already):
       - `Microsoft.MachineLearningServices/workspaces/marketplaceModelSubscriptions/*`  
       - `Microsoft.MachineLearningServices/workspaces/serverlessEndpoints/*`
 
@@ -90,12 +90,12 @@ If you need to deploy a different model, [deploy it to managed compute](#deploy-
 - An [AI Studio hub](../how-to/create-azure-ai-resource.md).
 
     > [!IMPORTANT]
-    > For Meta Llama 2 models, the pay-as-you-go model deployment offering is only available with AI hubs created in **East US 2** and **West US 3** regions.
+    > For Meta Llama 2 models, the pay-as-you-go model deployment offering is only available with hubs created in **East US 2** and **West US 3** regions.
 
 - 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 __owner__ or __contributor__ role for the Azure subscription. Alternatively, your account can be assigned a custom role that has the following permissions:
 
-    - On the Azure subscription—to subscribe the Azure AI project to the Azure Marketplace offering, once for each project, per offering:
+    - On the Azure subscription—to subscribe the AI Studio project to the Azure Marketplace offering, once for each project, per offering:
       - `Microsoft.MarketplaceOrdering/agreements/offers/plans/read`
       - `Microsoft.MarketplaceOrdering/agreements/offers/plans/sign/action`
       - `Microsoft.MarketplaceOrdering/offerTypes/publishers/offers/plans/agreements/read`
@@ -106,7 +106,7 @@ If you need to deploy a different model, [deploy it to managed compute](#deploy-
       - `Microsoft.SaaS/resources/read`
       - `Microsoft.SaaS/resources/write`
  
-    - On the Azure AI project—to deploy endpoints (the Azure AI Developer role contains these permissions already):
+    - On the AI Studio project—to deploy endpoints (the Azure AI Developer role contains these permissions already):
       - `Microsoft.MachineLearningServices/workspaces/marketplaceModelSubscriptions/*`  
       - `Microsoft.MachineLearningServices/workspaces/serverlessEndpoints/*`
 
diff --git a/articles/ai-studio/how-to/develop/index-build-consume-sdk.md b/articles/ai-studio/how-to/develop/index-build-consume-sdk.md
@@ -140,14 +140,14 @@ retriever.get_relevant_documents("<your search query>")
 retriever=get_langchain_retriever_from_index(local_index_cohere)
 retriever.get_relevant_documents("<your search query>")
 ```
-### Registering the index in your AI project (Optional)
+### Registering the index in your AI Studio project (Optional)
 
-Optionally, you can register the index in your AI project so that you or others who have access to your project can use it from the cloud. Before proceeding [install the required packages](#required-packages-for-remote-index-operations) for remote operations.
+Optionally, you can register the index in your AI Studio project so that you or others who have access to your project can use it from the cloud. Before proceeding [install the required packages](#required-packages-for-remote-index-operations) for remote operations.
 
 #### Connect to the project
 
 ```python
-# connect to the AI project
+# connect to the AI Studio project
 from azure.identity import DefaultAzureCredential
 from azure.ai.ml import MLClient
 
@@ -183,9 +183,9 @@ client.indexes.create_or_update(
 > [!NOTE]
 > Environment variables are intended for convenience in a local environment. However, if you register a local index created using environment variables, the index may not function as expected because secrets from environment variables won’t be transferred to the cloud index. To address this issue, you can use a `ConnectionConfig` or `connection_id` to create a local index before registering.
 
-## Build an index (remotely) in your AI project
+## Build an index (remotely) in your AI Studio project
 
-We build an index in the cloud in your AI project. 
+We build an index in the cloud in your AI Studio project. 
 
 ### Required packages for remote index operations
 
@@ -195,12 +195,12 @@ Install the following packages required for remote index creation.
 pip install azure-ai-ml promptflow-rag langchain langchain-openai
 ```
 
-### Connect to the AI project
+### Connect to the AI Studio project
 
 To get started, we connect to the project. The `subscription`, `resource_group` and `workspace` in the code below refers to the project you want to connect to.
 
 ```python
-# connect to the AI project
+# connect to the AI Studio project
 from azure.identity import DefaultAzureCredential
 from azure.ai.ml import MLClient
 
diff --git a/articles/ai-studio/how-to/develop/simulator-interaction-data.md b/articles/ai-studio/how-to/develop/simulator-interaction-data.md
@@ -29,7 +29,7 @@ pip install promptflow-evals
 
 from promptflow.evals.synthetic import AdversarialSimulator
 ```
-The adversarial simulator works by setting up a service-hosted GPT large language model to simulate an adversarial user and interact with your application. An Azure AI project is required to run the adversarial simulator:
+The adversarial simulator works by setting up a service-hosted GPT large language model to simulate an adversarial user and interact with your application. An AI Studio project is required to run the adversarial simulator:
 ```python
 from azure.identity import DefaultAzureCredential
 
diff --git a/articles/ai-studio/how-to/evaluate-flow-results.md b/articles/ai-studio/how-to/evaluate-flow-results.md
@@ -17,7 +17,7 @@ author: lgayhardt
 
 [!INCLUDE [Feature preview](../includes/feature-preview.md)]
 
-The Azure AI Studio evaluation page is a versatile hub that not only allows you to visualize and assess your results but also serves as a control center for optimizing, troubleshooting, and selecting the ideal AI model for your deployment needs. It's a one-stop solution for data-driven decision-making and performance enhancement in your AI projects. You can seamlessly access and interpret the results from various sources, including your flow, the playground quick test session, evaluation submission UI, generative SDK, and CLI. This flexibility ensures that you can interact with your results in a way that best suits your workflow and preferences.
+The Azure AI Studio evaluation page is a versatile hub that not only allows you to visualize and assess your results but also serves as a control center for optimizing, troubleshooting, and selecting the ideal AI model for your deployment needs. It's a one-stop solution for data-driven decision-making and performance enhancement in your AI Studio projects. You can seamlessly access and interpret the results from various sources, including your flow, the playground quick test session, evaluation submission UI, generative SDK, and CLI. This flexibility ensures that you can interact with your results in a way that best suits your workflow and preferences.
 
 Once you've visualized your evaluation results, you can dive into a thorough examination. This includes the ability to not only view individual results but also to compare these results across multiple evaluation runs. By doing so, you can identify trends, patterns, and discrepancies, gaining invaluable insights into the performance of your AI system under various conditions. 
 
diff --git a/articles/ai-studio/how-to/fine-tune-model-llama.md b/articles/ai-studio/how-to/fine-tune-model-llama.md
diff --git a/articles/ai-studio/what-is-ai-studio.md b/articles/ai-studio/what-is-ai-studio.md
