add Azure or portal

sdgilley · sdgilley · commit 0f1cb0cd8ef4 · 2024-11-26T12:45:39.000-06:00
diff --git a/articles/ai-studio/how-to/create-projects.md b/articles/ai-studio/how-to/create-projects.md
@@ -34,7 +34,7 @@ For more information about the projects and hubs model, see [Azure AI Foundry hu
 
 Use the following tabs to select the method you plan to use to create a project:
 
-# [Azure AI Foundry](#tab/ai-studio)
+# [AI Foundry portal](#tab/ai-studio)
 
 [!INCLUDE [Create Azure AI Foundry project](../includes/create-projects.md)]
 
@@ -85,7 +85,7 @@ The code in this section assumes you have an existing hub.  If you don't have a
 
 ## View project settings
 
-# [Azure AI Foundry](#tab/ai-studio)
+# [AI Foundry portal](#tab/ai-studio)
 
 On the project **Overview** page you can find information about the project.
 
diff --git a/articles/ai-studio/how-to/deploy-models-managed.md b/articles/ai-studio/how-to/deploy-models-managed.md
@@ -26,7 +26,7 @@ In this article, you learn how to deploy models using the Azure Machine Learning
 
 You can deploy managed compute models using the Azure Machine Learning SDK, but first, let's browse the model catalog and get the model ID you need for deployment.
 
-1. Sign in to [AI Foundry](https://ai.azure.com) and go to the **Home** page.
+1. Sign in to [Azure AI Foundry](https://ai.azure.com) and go to the **Home** page.
 1. Select **Model catalog** from the left sidebar.
 1. In the **Deployment options** filter, select **Managed compute**.
 
@@ -161,5 +161,5 @@ To deploy and perform inferencing with real-time endpoints, you consume Virtual
 
 ## Next steps
 
-- Learn more about what you can do in [AI Foundry](../what-is-ai-studio.md)
+- Learn more about what you can do in [Azure AI Foundry](../what-is-ai-studio.md)
 - Get answers to frequently asked questions in the [Azure AI FAQ article](../faq.yml)
diff --git a/articles/ai-studio/how-to/deploy-models-serverless-connect.md b/articles/ai-studio/how-to/deploy-models-serverless-connect.md
@@ -41,7 +41,7 @@ The need to consume a serverless API endpoint in a different project or hub than
 
 - You need to install the following software to work with Azure AI Foundry:
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     You can use any compatible web browser to navigate [Azure AI Foundry](https://ai.azure.com).
 
@@ -88,7 +88,7 @@ Follow these steps to create a connection:
 
 1. Connect to the project or hub where the endpoint is deployed:
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     Go to [Azure AI Foundry](https://ai.azure.com) and navigate to the project where the endpoint you want to connect to is deployed.
 
@@ -116,7 +116,7 @@ Follow these steps to create a connection:
 
 1. Get the endpoint's URL and credentials for the endpoint you want to connect to. In this example, you get the details for an endpoint name **meta-llama3-8b-qwerty**.
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     1. From the left sidebar of your project in AI Foundry portal, go to **My assets** > **Models + endpoints** to see the list of deployments in the project.
 
@@ -141,7 +141,7 @@ Follow these steps to create a connection:
 
 1. Now, connect to the project or hub **where you want to create the connection**:
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     Go to the project where the connection needs to be created to.
 
@@ -169,7 +169,7 @@ Follow these steps to create a connection:
 
 1. Create the connection in the project:
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     1. From the left sidebar of your project in AI Foundry portal, select **Management center**.
 
diff --git a/articles/ai-studio/how-to/deploy-models-serverless.md b/articles/ai-studio/how-to/deploy-models-serverless.md
@@ -35,7 +35,7 @@ This article uses a Meta Llama model deployment for illustration. However, you c
 
 - You need to install the following software to work with Azure AI Foundry:
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     You can use any compatible web browser to navigate [Azure AI Foundry](https://ai.azure.com).
 
@@ -132,7 +132,7 @@ Serverless API endpoints can deploy both Microsoft and non-Microsoft offered mod
 
 1. Create the model's marketplace subscription. When you create a subscription, you accept the terms and conditions associated with the model offer.
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     1. On the model's **Details** page, select **Deploy**. A **Deployment options** window opens up, giving you the choice between serverless API deployment and deployment using a managed compute.
 
@@ -259,7 +259,7 @@ Serverless API endpoints can deploy both Microsoft and non-Microsoft offered mod
 
 1. At any point, you can see the model offers to which your project is currently subscribed:
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     1. Go to the [Azure portal](https://portal.azure.com).
 
@@ -314,7 +314,7 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
 
 1. Create the serverless endpoint
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     1. To deploy a Microsoft model that doesn't require subscribing to a model offering:
         1. Select **Deploy** and then select **Serverless API with Azure AI Content Safety (preview)** to open the deployment wizard.
@@ -466,7 +466,7 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
 
 1. At any point, you can see the endpoints deployed to your project:
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     1. Go to your project.
 
@@ -515,7 +515,7 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
 
 1. The created endpoint uses key authentication for authorization. Use the following steps to get the keys associated with a given endpoint.
 
-    # [AI Foundry](#tab/azure-ai-studio)
+    # [AI Foundry portal](#tab/azure-ai-studio)
 
     You can select the deployment, and note the endpoint's _Target URI_ and _Key_. Use them to call the deployment and generate predictions.
 
@@ -573,7 +573,7 @@ To set the PNA flag for the Azure AI Foundry hub:
 
 You can delete model subscriptions and endpoints. Deleting a model subscription makes any associated endpoint become *Unhealthy* and unusable.
 
-# [AI Foundry](#tab/azure-ai-studio)
+# [AI Foundry portal](#tab/azure-ai-studio)
 
 To delete a serverless API endpoint:
 
diff --git a/articles/ai-studio/how-to/healthcare-ai/deploy-cxrreportgen.md b/articles/ai-studio/how-to/healthcare-ai/deploy-cxrreportgen.md
@@ -43,7 +43,7 @@ To use the CXRReportGen model, you need the following prerequisites:
 
 **Deployment to a self-hosted managed compute**
 
-CXRReportGen model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [AI Foundry](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
+CXRReportGen model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [Azure AI Foundry](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
 
 To __deploy the model through the UI__:
 
diff --git a/articles/ai-studio/how-to/healthcare-ai/deploy-medimageinsight.md b/articles/ai-studio/how-to/healthcare-ai/deploy-medimageinsight.md
@@ -41,7 +41,7 @@ To use the MedImageInsight model, you need the following prerequisites:
 
 **Deployment to a self-hosted managed compute**
 
-MedImageInsight model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [AI Foundry](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
+MedImageInsight model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [Azure AI Foundry](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
 
 To __deploy the model through the UI__:
 
diff --git a/articles/ai-studio/how-to/healthcare-ai/deploy-medimageparse.md b/articles/ai-studio/how-to/healthcare-ai/deploy-medimageparse.md
@@ -44,7 +44,7 @@ To use the MedImageParse model, you need the following prerequisites:
 
 **Deployment to a self-hosted managed compute**
 
-MedImageParse model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [AI Foundry](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
+MedImageParse model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [Azure AI Foundry](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
 
 To __deploy the model through the UI__:
 
