Merge pull request #3107 from Blackmist/links

v-shils · web-flow · commit 7786dbd37522 · 2025-02-21T12:37:03.000-08:00
link to portal
diff --git a/articles/ai-foundry/model-inference/how-to/configure-deployment-policies.md b/articles/ai-foundry/model-inference/how-to/configure-deployment-policies.md
@@ -15,7 +15,7 @@ ms.reviewer: fasantia
 
 # Control model deployment with custom policies
 
-When using models from Azure AI Services and Azure OpenAI with Azure AI Foundry, you might need to use custom policies to control which [type of deployment](../concepts/deployment-types.md) options are available to users or which specific models users can deploy. This article guides you on how to create policies to control model deployments using Azure Policies. 
+When using models from Azure AI Services and Azure OpenAI with [Azure AI Foundry](https://ai.azure.com), you might need to use custom policies to control which [type of deployment](../concepts/deployment-types.md) options are available to users or which specific models users can deploy. This article guides you on how to create policies to control model deployments using Azure Policies. 
 
 ## Prerequisites
 
diff --git a/articles/ai-studio/concepts/rbac-ai-studio.md b/articles/ai-studio/concepts/rbac-ai-studio.md
@@ -17,7 +17,7 @@ author: Blackmist
 
 # Role-based access control in Azure AI Foundry portal
 
-In this article, you learn how to manage access (authorization) to an Azure AI Foundry hub. Azure role-based access control (Azure RBAC) is used to manage access to Azure resources, such as the ability to create new resources or use existing ones. Users in your Microsoft Entra ID are assigned specific roles, which grant access to resources. Azure provides both built-in roles and the ability to create custom roles. 
+In this article, you learn how to manage access (authorization) to an [Azure AI Foundry](https://ai.azure.com) hub. Azure role-based access control (Azure RBAC) is used to manage access to Azure resources, such as the ability to create new resources or use existing ones. Users in your Microsoft Entra ID are assigned specific roles, which grant access to resources. Azure provides both built-in roles and the ability to create custom roles. 
 
 > [!WARNING]
 > Applying some roles might limit UI functionality in Azure AI Foundry portal for other users. For example, if a user's role does not have the ability to create a compute instance, the option to create a compute instance will not be available in studio. This behavior is expected, and prevents the user from attempting operations that would return an access denied error. 
diff --git a/articles/ai-studio/concepts/vulnerability-management.md b/articles/ai-studio/concepts/vulnerability-management.md
@@ -17,7 +17,7 @@ author: Blackmist
 
 Vulnerability management involves detecting, assessing, mitigating, and reporting on any security vulnerabilities that exist in an organization's systems and software. Vulnerability management is a shared responsibility between you and Microsoft.
 
-This article discusses these responsibilities and outlines the vulnerability management controls that Azure AI Foundry provides. You learn how to keep your service instance and applications up to date with the latest security updates, and how to minimize the window of opportunity for attackers.
+This article discusses these responsibilities and outlines the vulnerability management controls that [Azure AI Foundry](https://ai.azure.com) provides. You learn how to keep your service instance and applications up to date with the latest security updates, and how to minimize the window of opportunity for attackers.
 
 ## Microsoft-managed VM images
 
diff --git a/articles/ai-studio/how-to/access-on-premises-resources.md b/articles/ai-studio/how-to/access-on-premises-resources.md
@@ -14,7 +14,7 @@ author: Blackmist
 
 # Access on-premises resources from your Azure AI Foundry's managed network (preview)
 
-To access your non-Azure resources located in a different virtual network or located entirely on-premises from your Azure AI Foundry's managed virtual network, an Application Gateway must be configured. Through this Application Gateway, full end to end access can be configured to your resources.
+To access your non-Azure resources located in a different virtual network or located entirely on-premises from your [Azure AI Foundry](https://ai.azure.com)'s managed virtual network, an Application Gateway must be configured. Through this Application Gateway, full end to end access can be configured to your resources.
 
 Azure Application Gateway is a load balancer that makes routing decisions based on the URL of an HTTPS request. Azure Machine Learning supports using an application gateway to securely communicate with non-Azure resources. For more on Application Gateway, see [What is Azure Application Gateway](/azure/application-gateway/overview).
 
diff --git a/articles/ai-studio/how-to/autoscale.md b/articles/ai-studio/how-to/autoscale.md
@@ -16,7 +16,7 @@ author: Blackmist
 
 # Autoscale Azure AI limits
 
-This article provides guidance for how you can manage and increase quotas for Azure AI services resources with Azure AI Foundry.
+This article provides guidance for how you can manage and increase quotas for Azure AI services resources with [Azure AI Foundry](https://ai.azure.com).
 
 ## Overview
 
diff --git a/articles/ai-studio/how-to/azure-policy.md b/articles/ai-studio/how-to/azure-policy.md
@@ -12,7 +12,7 @@ ms.topic: how-to
 
 # Audit and manage Azure AI Foundry hubs and projects
 
-As a platform administrator, you can use policies to lay out guardrails for teams to manage their own resources. [Azure Policy](/azure/governance/policy/) helps audit and govern resource state. This article explains how you can use audit controls and governance practices for Azure AI Foundry.
+As a platform administrator, you can use policies to lay out guardrails for teams to manage their own resources. [Azure Policy](/azure/governance/policy/) helps audit and govern resource state. This article explains how you can use audit controls and governance practices for [Azure AI Foundry](https://ai.azure.com).
 
 ## Policies for Azure AI Foundry hubs and projects
 
diff --git a/articles/ai-studio/how-to/configure-managed-network.md b/articles/ai-studio/how-to/configure-managed-network.md
@@ -15,7 +15,7 @@ zone_pivot_groups: azure-ai-studio-sdk-cli
 
 # How to configure a managed network for Azure AI Foundry hubs
 
-We have two network isolation aspects. One is the network isolation to access an Azure AI Foundry hub. Another is the network isolation of computing resources for both your hub and project (such as compute instance, serverless and managed online endpoint.) This document explains the latter highlighted in the diagram. You can use hub built-in network isolation to protect your computing resources.
+We have two network isolation aspects. One is the network isolation to access an [Azure AI Foundry](https://ai.azure.com) hub. Another is the network isolation of computing resources for both your hub and project (such as compute instance, serverless and managed online endpoint.) This document explains the latter highlighted in the diagram. You can use hub built-in network isolation to protect your computing resources.
 
 :::image type="content" source="../media/how-to/network/azure-ai-network-outbound.svg" alt-text="Diagram of hub network isolation." lightbox="../media/how-to/network/azure-ai-network-outbound.png":::
 
diff --git a/articles/ai-studio/how-to/configure-private-link.md b/articles/ai-studio/how-to/configure-private-link.md
@@ -15,7 +15,7 @@ author: Blackmist
 
 # How to configure a private link for Azure AI Foundry hubs
 
-We have two network isolation aspects. One is the network isolation to access an Azure AI Foundry hub. Another is the network isolation of computing resources in your hub and projects such as compute instances, serverless, and managed online endpoints. This article explains the former highlighted in the diagram. You can use private link to establish the private connection to your hub and its default resources. This article is for Azure AI Foundry (hub and projects). For information on Azure AI services, see the [Azure AI services documentation](/azure/ai-services/cognitive-services-virtual-networks).
+We have two network isolation aspects. One is the network isolation to access an [Azure AI Foundry](https://ai.azure.com) hub. Another is the network isolation of computing resources in your hub and projects such as compute instances, serverless, and managed online endpoints. This article explains the former highlighted in the diagram. You can use private link to establish the private connection to your hub and its default resources. This article is for Azure AI Foundry (hub and projects). For information on Azure AI services, see the [Azure AI services documentation](/azure/ai-services/cognitive-services-virtual-networks).
 
 :::image type="content" source="../media/how-to/network/azure-ai-network-inbound.svg" alt-text="Diagram of Azure AI Foundry hub network isolation." lightbox="../media/how-to/network/azure-ai-network-inbound.png":::
 
diff --git a/articles/ai-studio/how-to/connections-add.md b/articles/ai-studio/how-to/connections-add.md
@@ -22,11 +22,11 @@ author: Blackmist
 
 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](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. 
+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. 
 
 ## Connection types
 
-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.
+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.
 
 | Service connection type | Preview | Description |
 | --- |:---:| --- |
@@ -36,7 +36,7 @@ Here's a table of some of the available connection types in [Azure AI Foundry po
 | 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](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). |
+| 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). |
 | 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. |
 
diff --git a/articles/ai-studio/how-to/costs-plan-manage.md b/articles/ai-studio/how-to/costs-plan-manage.md
@@ -19,7 +19,7 @@ author: Blackmist
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-This article describes how you plan for and manage costs for Azure AI Foundry. First, you use the Azure pricing calculator to help plan for Azure AI Foundry costs before you add any resources for the service to estimate costs. Next, as you add Azure resources, review the estimated costs.
+This article describes how you plan for and manage costs for [Azure AI Foundry](https://ai.azure.com). First, you use the Azure pricing calculator to help plan for Azure AI Foundry costs before you add any resources for the service to estimate costs. Next, as you add Azure resources, review the estimated costs.
 
 > [!TIP]
 > Azure AI Foundry does not have a specific page in the Azure pricing calculator. Azure AI Foundry is composed of several other Azure services, some of which are optional. This article provides information on using the pricing calculator to estimate costs for these services.
@@ -60,7 +60,7 @@ When you create resources for a hub, resources for other Azure services are also
 | [Azure Machine Learning](https://azure.microsoft.com/pricing/details/machine-learning/) | Compute instances are needed to run [Visual Studio Code (Web or Desktop)](./develop/vscode.md) and [prompt flow](./prompt-flow.md) via Azure AI Foundry.<br/><br/>When you create a compute instance, the virtual machine (VM) stays on so it's available for your work.<br/><br/>Enable idle shutdown to save on cost when the VM is idle for a specified time period.<br/><br/>Or set up a schedule to automatically start and stop the compute instance to save cost when you aren't planning to use it. | 
 | [Azure Virtual Machine](https://azure.microsoft.com/pricing/details/virtual-machines/) | Azure Virtual Machines gives you the flexibility of virtualization for a wide range of computing solutions with support for Linux, Windows Server, SQL Server, Oracle, IBM, SAP, and more. |
 | [Azure Container Registry Basic account](https://azure.microsoft.com/pricing/details/container-registry) | Provides storage of private Docker container images, enabling fast, scalable retrieval, and network-close deployment of container workloads on Azure. |
-| [Azure Blob Storage](https://azure.microsoft.com/pricing/details/storage/blobs/) | Can be used to store [Azure AI Foundry project](./create-projects.md) files. |
+| [Azure Blob Storage](https://azure.microsoft.com/pricing/details/storage/blobs/) | Can be used to store Azure AI Foundry project files. |
 | [Key Vault](https://azure.microsoft.com/pricing/details/key-vault/) | A key vault for storing secrets. |
 | [Azure Private Link](https://azure.microsoft.com/pricing/details/private-link/) | Azure Private Link enables you to access Azure PaaS Services (for example, Azure Storage and SQL Database) over a private endpoint in your virtual network. |
 
@@ -97,10 +97,10 @@ When you use cost analysis, you view hub costs in graphs and tables for differen
 
 ### Monitor Azure AI Foundry project costs
 
-You can get to cost analysis from the [Azure portal](https://portal.azure.com). You can also get to cost analysis from the [Azure AI Foundry](https://ai.azure.com).
+You can get to cost analysis from the [Azure portal](https://portal.azure.com). You can also get to cost analysis from the [Azure AI Foundry portal](https://ai.azure.com).
 
 > [!IMPORTANT]
-> Your Azure AI Foundry project costs are only a subset of your overall application or solution costs. You need to monitor costs for all Azure resources used in your application or solution. For more information, see [Azure AI Foundry hubs](../concepts/ai-resources.md).
+> Your Azure AI Foundry project costs are only a subset of your overall application or solution costs. You need to monitor costs for all Azure resources used in your application or solution. For more information, see Azure AI Foundry hubs.
 
 For the examples in this section, assume that all Azure AI Foundry resources are in the same resource group. But you can have resources in different resource groups. For example, your Azure AI Search resource might be in a different resource group than your project.
 
diff --git a/articles/ai-studio/how-to/create-azure-ai-resource.md b/articles/ai-studio/how-to/create-azure-ai-resource.md
@@ -18,7 +18,7 @@ author: Blackmist
 
 # How to create and manage an Azure AI Foundry hub
 
-In Azure AI Foundry portal, hubs provide the environment for a team to collaborate and organize work, and help you as a team lead or IT admin centrally set up security settings and govern usage and spend. You can create and manage a hub from the Azure portal or from the Azure AI Foundry portal, and then your developers can create projects from the hub.
+In [Azure AI Foundry portal](https://ai.azure.com), hubs provide the environment for a team to collaborate and organize work, and help you as a team lead or IT admin centrally set up security settings and govern usage and spend. You can create and manage a hub from the Azure portal or from the Azure AI Foundry portal, and then your developers can create projects from the hub.
 
 In this article, you learn how to create and manage a hub in Azure AI Foundry portal with the default settings so you can get started quickly. Do you need to customize security or the dependent resources of your hub? Then use [Azure portal](create-secure-ai-hub.md) or [template options](create-azure-ai-hub-template.md). 
 
diff --git a/articles/ai-studio/how-to/create-hub-terraform.md b/articles/ai-studio/how-to/create-hub-terraform.md
@@ -27,8 +27,8 @@ In this article, you use Terraform to create an [Azure AI Foundry](https://ai.az
 > * Set up a storage account
 > * Establish a key vault
 > * Configure AI services
-> * Build an [Azure AI Foundry](https://ai.azure.com) hub
-> * Develop an [Azure AI Foundry](https://ai.azure.com) project
+> * Build an Azure AI Foundry hub
+> * Develop an Azure AI Foundry project
 > * Establish an AI services connection
 
 ## Prerequisites
diff --git a/articles/ai-studio/how-to/develop/connections-add-sdk.md b/articles/ai-studio/how-to/develop/connections-add-sdk.md
@@ -18,7 +18,7 @@ author: Blackmist
 
 [!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
-In this article, you learn how to add a new connection using the Azure Machine Learning SDK.
+In this article, you learn how to add a new connection to [Azure AI Foundry](https://ai.azure.com) using the Azure Machine Learning SDK.
 
 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. For more information, see [Connections in Azure AI Foundry portal](../../concepts/connections.md).
 
diff --git a/articles/ai-studio/how-to/disable-local-auth.md b/articles/ai-studio/how-to/disable-local-auth.md
@@ -15,7 +15,7 @@ ms.reviewer: ambadal
 
 # Disable shared key access for your hub's storage account (preview)
 
-An Azure AI Foundry hub defaults to use of a shared key to access its default Azure Storage account. With key-based authorization, anyone who has the key and access to the storage account can access data.
+An [Azure AI Foundry](https://ai.azure.com) hub defaults to use of a shared key to access its default Azure Storage account. With key-based authorization, anyone who has the key and access to the storage account can access data.
 
 To reduce the risk of unauthorized access, you can disable key-based authorization, and instead use Microsoft Entra ID for authorization. This configuration uses a Microsoft Entra ID value to authorize access to the storage account. The identity used to access storage is either the user's identity or a managed identity. The user's identity is used to view data in the Azure Machine Learning studio, or to run a notebook while authenticated with the user's identity. The Azure Machine Learning service uses a managed identity to access the storage account - for example, when running a training job as the managed identity.
 
diff --git a/articles/ai-studio/how-to/disaster-recovery.md b/articles/ai-studio/how-to/disaster-recovery.md
diff --git a/articles/ai-studio/how-to/quota.md b/articles/ai-studio/how-to/quota.md
diff --git a/articles/ai-studio/how-to/secure-data-playground.md b/articles/ai-studio/how-to/secure-data-playground.md
diff --git a/articles/ai-studio/how-to/troubleshoot-secure-connection-project.md b/articles/ai-studio/how-to/troubleshoot-secure-connection-project.md
