list type

Author: JamesJBarnett

articles/ai-foundry/concepts/architecture.md

@@ -52,9 +52,9 @@ Azure AI Foundry applies a flexible compute architecture to support diverse [mod
 
 - Model Hosting Architecture: Foundry models access is provided in different ways:
 
-  1. [Standard deployment in Azure AI Foundry resources](deployments-overview.md#standard-deployment-in-azure-ai-foundry-resources)
-  1. [Deployment to serverless API endpoints in Azure AI Hub resources](deployments-overview.md#serverless-api-endpoint)
-  1. [Deployment to managed computes in Azure AI Hub resources](deployments-overview.md#managed-compute)
+  - [Standard deployment in Azure AI Foundry resources](deployments-overview.md#standard-deployment-in-azure-ai-foundry-resources)
+  - [Deployment to serverless API endpoints in Azure AI Hub resources](deployments-overview.md#serverless-api-endpoint)
+  - [Deployment to managed computes in Azure AI Hub resources](deployments-overview.md#managed-compute)
 
   For an overview of data, privacy, and security considerations with these deployment options, see [Data, privacy, and security for use of models](../how-to/concept-data-privacy.md)
 
@@ -89,4 +89,4 @@ Users can optionally connect their own Azure Storage accounts. Foundry tools can
 * [Azure AI Foundry rollout across my organization](planning.md)
 * [Customer-managed keys for encryption with Azure AI Foundry](encryption-keys-portal.md)
 * [How to configure a private link for Azure AI Foundry](../how-to/configure-private-link.md)
-* [Bring-your-own resources with the Agent service](../agents/how-to/use-your-own-resources.md)
+* [Bring-your-own resources with the Agent service](../agents/how-to/use-your-own-resources.md)