link to portal

Blackmist · Blackmist · commit 0fe6e9a3f10b · 2025-02-21T11:08:06.000-05:00
diff --git a/articles/ai-studio/how-to/connections-add.md b/articles/ai-studio/how-to/connections-add.md
@@ -20,13 +20,13 @@ author: Blackmist
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-In this article, you learn how to add a new connection in Azure AI Foundry portal.
+In this article, you learn how to add a new connection in [Azure AI Foundry portal](https://ai.azure.com).
 
-Connections are a way to authenticate and consume both Microsoft and other resources within your Azure AI Foundry projects. For example, connections can be used for prompt flow, training data, and deployments. [Connections can be created](../how-to/connections-add.md) exclusively for one project or shared with all projects in the same Azure AI Foundry hub. 
+Connections are a way to authenticate and consume both Microsoft and other resources within your [Azure AI Foundry](https://ai.azure.com) projects. For example, connections can be used for prompt flow, training data, and deployments. [Connections can be created](../how-to/connections-add.md) exclusively for one project or shared with all projects in the same [Azure AI Foundry](https://ai.azure.com) hub. 
 
 ## Connection types
 
-Here's a table of some of the available connection types in Azure AI Foundry portal. The __Preview__ column indicates connection types that are currently in preview.
+Here's a table of some of the available connection types in [Azure AI Foundry portal]((https://ai.azure.com). The __Preview__ column indicates connection types that are currently in preview.
 
 | Service connection type | Preview | Description |
 | --- |:---:| --- |
@@ -36,15 +36,15 @@ Here's a table of some of the available connection types in Azure AI Foundry por
 | Azure Content Safety | | Azure AI Content Safety is a service that detects potentially unsafe content in text, images, and videos. |
 | Azure OpenAI || Azure OpenAI is a service that provides access to OpenAI's models including the GPT-4o, GPT-4o mini, GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, DALLE-3 and Embeddings model series with the security and enterprise capabilities of Azure. |
 | Serverless Model | ✓ | Serverless Model connections allow you to [serverless API deployment](deploy-models-serverless.md). |
-| Microsoft OneLake | | Microsoft OneLake provides open access to all of your Fabric items through Azure Data Lake Storage (ADLS) Gen2 APIs and SDKs.<br/><br/>In Azure AI Foundry portal, you can set up a connection to your OneLake data using a OneLake URI. You can find the information that Azure AI Foundry requires to construct a __OneLake Artifact URL__ (workspace and item GUIDs) in the URL on the Fabric portal. For information about the URI syntax, see [Connecting to Microsoft OneLake](/fabric/onelake/onelake-access-api). |
+| Microsoft OneLake | | Microsoft OneLake provides open access to all of your Fabric items through Azure Data Lake Storage (ADLS) Gen2 APIs and SDKs.<br/><br/>In [Azure AI Foundry portal](https://ai.azure.com), you can set up a connection to your OneLake data using a OneLake URI. You can find the information that [Azure AI Foundry](https://ai.azure.com) requires to construct a __OneLake Artifact URL__ (workspace and item GUIDs) in the URL on the Fabric portal. For information about the URI syntax, see [Connecting to Microsoft OneLake](/fabric/onelake/onelake-access-api). |
 | API key || API Key connections handle authentication to your specified target on an individual basis. For example, you can use this connection with the SerpApi tool in prompt flow.  |
 | Custom || Custom connections allow you to securely store and access keys while storing related properties, such as targets and versions. Custom connections are useful when you have many targets that or cases where you wouldn't need a credential to access. LangChain scenarios are a good example where you would use custom service connections. Custom connections don't manage authentication, so you have to manage authentication on your own. |
 
 ## Create a new connection
 
 Follow these steps to create a new connection that's only available for the current project.
 
-1. Go to your project in Azure AI Foundry portal. If you don't have a project, [create a new project](./create-projects.md).
+1. Go to your project in [Azure AI Foundry portal](https://ai.azure.com). If you don't have a project, [create a new project](./create-projects.md).
 1. Select __Management center__ from the bottom left navigation.
 1. Select __Connected resources__ from the __Project__ section.
 1. Select __+ New connection__ from the __Connected resources__ section.
diff --git a/articles/ai-studio/how-to/create-azure-ai-hub-template.md b/articles/ai-studio/how-to/create-azure-ai-hub-template.md
@@ -17,7 +17,7 @@ author: Blackmist
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-Use a [Microsoft Bicep](/azure/azure-resource-manager/bicep/overview) template to create a hub for Azure AI Foundry. A template makes it easy to create resources as a single, coordinated operation. A Bicep template is a text document that defines the resources that are needed for a deployment. It might also specify deployment parameters. Parameters are used to provide input values when using the template.
+Use a [Microsoft Bicep](/azure/azure-resource-manager/bicep/overview) template to create a hub for [Azure AI Foundry](https://ai.azure.com). A template makes it easy to create resources as a single, coordinated operation. A Bicep template is a text document that defines the resources that are needed for a deployment. It might also specify deployment parameters. Parameters are used to provide input values when using the template.
 
 The template used in this article can be found at [https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/aistudio-basics](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/aistudio-basics). Both the source `main.bicep` file and the compiled Azure Resource Manager template (`main.json`) file are available. This template creates the following resources:
 
diff --git a/articles/ai-studio/how-to/create-hub-terraform.md b/articles/ai-studio/how-to/create-hub-terraform.md
@@ -18,7 +18,7 @@ ai-usage: ai-assisted
 
 # Use Terraform to create an Azure AI Foundry hub
 
-In this article, you use Terraform to create an Azure AI Foundry hub, a project, and AI services connection. A hub is a central place for data scientists and developers to collaborate on machine learning projects. It provides a shared, collaborative space to build, train, and deploy machine learning models. The hub is integrated with Azure Machine Learning and other Azure services, making it a comprehensive solution for machine learning tasks. The hub also allows you to manage and monitor your AI deployments, ensuring they're performing as expected.
+In this article, you use Terraform to create an [Azure AI Foundry](https://ai.azure.com) hub, a project, and AI services connection. A hub is a central place for data scientists and developers to collaborate on machine learning projects. It provides a shared, collaborative space to build, train, and deploy machine learning models. The hub is integrated with Azure Machine Learning and other Azure services, making it a comprehensive solution for machine learning tasks. The hub also allows you to manage and monitor your AI deployments, ensuring they're performing as expected.
 
 [!INCLUDE [About Terraform](~/azure-dev-docs-pr/articles/terraform/includes/abstract.md)]
 
@@ -27,8 +27,8 @@ In this article, you use Terraform to create an Azure AI Foundry hub, a project,
 > * Set up a storage account
 > * Establish a key vault
 > * Configure AI services
-> * Build an Azure AI Foundry hub
-> * Develop an Azure AI Foundry project
+> * Build an [Azure AI Foundry](https://ai.azure.com) hub
+> * Develop an [Azure AI Foundry](https://ai.azure.com) project
 > * Establish an AI services connection
 
 ## Prerequisites
diff --git a/articles/ai-studio/how-to/create-secure-ai-hub.md b/articles/ai-studio/how-to/create-secure-ai-hub.md
@@ -15,7 +15,7 @@ author: Blackmist
 
 # How to create a secure Azure AI Foundry hub and project with a managed virtual network
 
-You can secure your Azure AI Foundry hub, projects, and managed resources in a managed virtual network. With a managed virtual network, inbound access is only allowed through a private endpoint for your hub. Outbound access can be configured to allow either all outbound access, or only allowed outbound that you specify. For more information, see [Managed virtual network](configure-managed-network.md).
+You can secure your [Azure AI Foundry](https://ai.azure.com) hub, projects, and managed resources in a managed virtual network. With a managed virtual network, inbound access is only allowed through a private endpoint for your hub. Outbound access can be configured to allow either all outbound access, or only allowed outbound that you specify. For more information, see [Managed virtual network](configure-managed-network.md).
 
 > [!IMPORTANT]
 > The managed virtual network doesn't provide inbound connectivity for your clients. For more information, see the [Connect to the hub](#connect-to-the-hub) section. 
