fixing links

Author: Blackmist

articles/ai-foundry/concepts/concept-model-distillation.md

@@ -41,6 +41,6 @@ We used an advanced prompt during synthetic data generation. The advanced prompt
 
 ## Related content
 
-- [What is Azure AI Foundry?](../what-is-ai-studio.md)
+- [What is Azure AI Foundry?](../what-is-ai-foundry.md)
 - [Deploy Meta Llama 3.1 models with Azure AI Foundry](../how-to/deploy-models-llama.md)
 - [Azure AI Foundry FAQ](../faq.yml)

articles/ai-foundry/concepts/concept-synthetic-data.md

@@ -35,6 +35,6 @@ To see how to generate synthetic data, you can use the [sample notebook](https:/
 
 ## Related content
 
-- [What is Azure AI Foundry?](../what-is-ai-studio.md)
+- [What is Azure AI Foundry?](../what-is-ai-foundry.md)
 - [Deploy Meta Llama 3.1 models with Azure AI Foundry](../how-to/deploy-models-llama.md)
 - [Azure AI Foundry FAQ](../faq.yml)

articles/ai-foundry/concepts/connections.md

@@ -54,7 +54,7 @@ When you create a connection with an existing Azure storage account, you can cho
 - **Identity-based**: Use your Microsoft Entra ID or managed identity to authenticate data access.
 
     > [!TIP]
-    > When using an identity-based connection, Azure role-based access control (Azure RBAC) is used to determine who can access the connection. You must assign the correct Azure RBAC roles to your developers before they can use the connection. For more information, see [Scenario: Connections using Microsoft Entra ID](rbac-ai-studio.md#scenario-connections-using-microsoft-entra-id-authentication).
+    > When using an identity-based connection, Azure role-based access control (Azure RBAC) is used to determine who can access the connection. You must assign the correct Azure RBAC roles to your developers before they can use the connection. For more information, see [Scenario: Connections using Microsoft Entra ID](rbac-ai-foundry.md#scenario-connections-using-microsoft-entra-id-authentication).
 
 
 The following table shows the supported Azure cloud-based storage services and authentication methods:
@@ -84,7 +84,7 @@ A Uniform Resource Identifier (URI) represents a storage location on your local
 
 Connections allow you to securely store credentials, authenticate access, and consume data and information.  Secrets associated with connections are securely persisted in the corresponding Azure Key Vault, adhering to robust security and compliance standards. As an administrator, you can audit both shared and project-scoped connections on a hub level (link to connection rbac). 
 
-Azure connections serve as key vault proxies, and interactions with connections are direct interactions with an Azure key vault. Azure AI Foundry connections store API keys securely, as secrets, in a key vault. The key vault [Azure role-based access control (Azure RBAC)](./rbac-ai-studio.md) controls access to these connection resources. A connection references the credentials from the key vault storage location for further use. You won't need to directly deal with the credentials after they're stored in the hub's key vault. You have the option to store the credentials in the YAML file. A CLI command or SDK can override them. We recommend that you avoid credential storage in a YAML file, because a security breach could lead to a credential leak.  
+Azure connections serve as key vault proxies, and interactions with connections are direct interactions with an Azure key vault. Azure AI Foundry connections store API keys securely, as secrets, in a key vault. The key vault [Azure role-based access control (Azure RBAC)](./rbac-ai-foundry.md) controls access to these connection resources. A connection references the credentials from the key vault storage location for further use. You won't need to directly deal with the credentials after they're stored in the hub's key vault. You have the option to store the credentials in the YAML file. A CLI command or SDK can override them. We recommend that you avoid credential storage in a YAML file, because a security breach could lead to a credential leak.  
 
 
 ## Next steps

articles/ai-foundry/concepts/management-center.md

@@ -40,10 +40,10 @@ Assign roles, manage users, and ensure that all settings comply with organizatio
 
 :::image type="content" source="../media/management-center/user-management.png" alt-text="Screenshot of the user management section of the management center." lightbox="../media/management-center/user-management.png":::
 
-For more information, see [Role-based access control](rbac-ai-studio.md#assigning-roles-in-azure-ai-foundry-portal).
+For more information, see [Role-based access control](rbac-ai-foundry.md#assigning-roles-in-azure-ai-foundry-portal).
 
 ## Related content
 
 - [Security baseline](/security/benchmark/azure/baselines/azure-ai-studio-security-baseline)
 - [Built-in policy to allow specific models](../how-to/built-in-policy-model-deployment.md)
-- [Custom policy to allow specific models](../how-to/custom-policy-model-deployment.md)
+- [Custom policy to allow specific models](../model-inference/how-to/configure-deployment-policies.md)

articles/ai-services/openai/how-to/use-blocklists.md

@@ -253,7 +253,7 @@ If the completion itself is blocked, the response returns `200`, as the completi
 
 #### [Azure AI Foundry](#tab/foundry)
 
-[!INCLUDE [use-blocklists](../../../ai-studio/includes/use-blocklists.md)]
+[!INCLUDE [use-blocklists](../../../ai-foundry/includes/use-blocklists.md)]
 
 ---
 

articles/machine-learning/concept-endpoint-serverless-availability.md

@@ -27,7 +27,7 @@ Certain models in the model catalog can be deployed as a serverless API with pay
 
 Pay-as-you-go billing is available only to users whose Azure subscription belongs to a billing account in a country/region where the model provider has made the offer available (see "offer availability region" in the table in the next section). If the offer is available in the relevant region, the user then must have a Hub/Project in the Azure region where the model is available for deployment or fine-tuning, as applicable (see "Hub/Project Region" columns in the following tables).
 
-[!INCLUDE [region-availability-maas](../ai-studio/includes/region-availability-maas.md)]
+[!INCLUDE [region-availability-maas](../ai-foundry/includes/region-availability-maas.md)]
 
 
 ## Alternatives to region availability