hub and project terminology

eric-urban · eric-urban · commit e1f7d914df69 · 2024-08-14T11:27:06.000-07:00
diff --git a/articles/ai-studio/concepts/architecture.md b/articles/ai-studio/concepts/architecture.md
@@ -19,13 +19,13 @@ AI Studio provides a unified experience for AI developers and data scientists to
 
 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:
+- **AI Studio 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 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:
+- **AI Studio 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:
     - Access to development tools for building and customizing AI applications.   
     - Reusable components including datasets, models, and indexes.
     - An isolated container to upload data to (within the storage inherited from the hub).
diff --git a/articles/ai-studio/how-to/create-hub-terraform.md b/articles/ai-studio/how-to/create-hub-terraform.md
@@ -1,6 +1,6 @@
 ---
 title: 'Use Terraform to create an Azure AI Studio hub'
-description: In this article, you create an Azure AI hub, an AI project, an AI services resource, and more resources.
+description: In this article, you create an Azure AI Studio hub, an Azure AI Studio project, an AI services resource, and more resources.
 ms.topic: how-to
 ms.date: 07/12/2024
 titleSuffix: Azure AI Studio 
@@ -27,8 +27,8 @@ In this article, you use Terraform to create an Azure AI Studio hub, a project,
 > * Set up a storage account
 > * Establish a key vault
 > * Configure AI services
-> * Build an Azure AI hub
-> * Develop an AI project
+> * Build an AI Studio hub
+> * Develop an AI Studio project
 > * Establish an AI services connection
 
 ## Prerequisites
diff --git a/articles/ai-studio/how-to/create-projects.md b/articles/ai-studio/how-to/create-projects.md
@@ -63,7 +63,7 @@ Use the following tabs to select the method you plan to use to create a project:
 
     For more information on authenticating, see [Authentication methods](/cli/azure/authenticate-azure-cli).
 
-1. Once the extension is installed and authenticated to your Azure subscription, use the following command to create a new Azure AI project from an existing Azure AI hub:
+1. Once the extension is installed and authenticated to your Azure subscription, use the following command to create a new Azure AI Studio project from an existing Azure AI Studio hub:
 
     ```azurecli
     az ml workspace create --kind project --hub-id {my_hub_ARM_ID} --resource-group {my_resource_group} --name {my_project_name}
diff --git a/articles/ai-studio/how-to/deploy-models-serverless.md b/articles/ai-studio/how-to/deploy-models-serverless.md
@@ -556,11 +556,11 @@ Read more about the [capabilities of this API](../reference/reference-model-infe
 
 Endpoints for models deployed as Serverless APIs follow the public network access (PNA) flag setting of the AI Studio Hub that has the project in which the deployment exists. To secure your MaaS endpoint, disable the PNA flag on your AI Studio Hub. You can secure inbound communication from a client to your endpoint by using a private endpoint for the hub.
 
-To set the PNA flag for the Azure AI hub:
+To set the PNA flag for the Azure AI Studio hub:
 
 1. Go to the [Azure portal](https://portal.azure.com).
-2. Search for the Resource group to which the hub belongs, and select your Azure AI hub from the resources listed for this Resource group.
-3. On the hub Overview page, use the left navigation pane to go to Settings > Networking.
+2. Search for the Resource group to which the hub belongs, and select the **Azure AI hub** from the resources listed for this resource group.
+3. From the hub **Overview** page on the left menu, select **Settings** > **Networking**.
 4. Under the **Public access** tab, you can configure settings for the public network access flag.
 5. Save your changes. Your changes might take up to five minutes to propagate.
 
diff --git a/articles/ai-studio/how-to/develop/flow-evaluate-sdk.md b/articles/ai-studio/how-to/develop/flow-evaluate-sdk.md
@@ -221,7 +221,7 @@ ml_client.evaluators.download("answer_len_uploaded", version=1, download_path=".
 evaluator = load_flow(os.path.join("answer_len_uploaded", flex_flow_path))
 ```
 
-After logging your custom evaluator to your AI project, you can view it in your [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) under Evaluation tab in AI studio.
+After logging your custom evaluator to your AI Studio project, you can view it in your [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) under Evaluation tab in AI studio.
 
 ### Prompt-based evaluators
 
@@ -307,7 +307,7 @@ ml_client.evaluators.download("prompty_uploaded", version=1, download_path=".")
 evaluator = load_flow(os.path.join("prompty_uploaded", "apology.prompty"))
 ```
 
-After logging your custom evaluator to your AI project, you can view it in your [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) under Evaluation tab in AI studio.
+After logging your custom evaluator to your AI Studio project, you can view it in your [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) under **Evaluation** tab in AI Studio.
 
 ## Evaluate on test dataset using `evaluate()`
 
diff --git a/articles/ai-studio/how-to/fine-tune-model-llama.md b/articles/ai-studio/how-to/fine-tune-model-llama.md
@@ -56,15 +56,15 @@ Fine-tuning of Llama 2 models is currently supported in projects located in West
 
 
  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 [Azure AI Studio hub](../how-to/create-azure-ai-resource.md).
 
     > [!IMPORTANT]
-    > For Meta Llama 3.1 models, the pay-as-you-go model fine-tune offering is only available with AI hubs created in **West US 3** regions.
+    > For Meta Llama 3.1 models, the pay-as-you-go model fine-tune offering is only available with hubs created in **West US 3** regions.
 
-- An [Azure AI project](../how-to/create-projects.md) in Azure AI Studio.
+- An [Azure 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`
@@ -75,7 +75,7 @@ Fine-tuning of Llama 2 models is currently supported in projects located in West
       - `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/fine-tune-phi-3.md b/articles/ai-studio/how-to/fine-tune-phi-3.md
@@ -78,7 +78,7 @@ Verify the subscription is registered to the `Microsoft.Network` resource provid
 1. Sign in to the [Azure portal](https://portal.azure.com).
 1. Select **Subscriptions** from the left menu.
 1. Select the subscription you want to use.
-1. Select **AI project settings** > **Resource providers** from the left menu.
+1. Select **Settings** > **Resource providers** from the left menu.
 1. Confirm that **Microsoft.Network** is in the list of resource providers. Otherwise add it.
 
 
