fixing links

Blackmist · Blackmist · commit 50ddd1dcd0cd · 2025-02-26T15:42:41.000-05:00
diff --git a/articles/ai-foundry/how-to/deploy-models-jamba.md b/articles/ai-foundry/how-to/deploy-models-jamba.md
@@ -66,7 +66,7 @@ To get started with Jamba 1.5 mini deployed as a serverless API, explore our int
       - `Microsoft.MachineLearningServices/workspaces/marketplaceModelSubscriptions/*`  
       - `Microsoft.MachineLearningServices/workspaces/serverlessEndpoints/*`
 
-    For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-studio.md).
+    For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-foundry.md).
 
 
 ### Create a new deployment
@@ -402,6 +402,6 @@ Models deployed as a serverless API are protected by Azure AI content safety. Wi
 
 ## Related content
 
-- [What is Azure AI Foundry?](../what-is-ai-studio.md)
+- [What is Azure AI Foundry?](../what-is-ai-foundry.md)
 - [Azure AI FAQ article](../faq.yml)
 - [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)
diff --git a/articles/ai-foundry/how-to/deploy-models-managed.md b/articles/ai-foundry/how-to/deploy-models-managed.md
@@ -165,5 +165,5 @@ To deploy and perform inferencing with real-time endpoints, you consume Virtual
 
 ## Next steps
 
-- Learn more about what you can do in [Azure AI Foundry](../what-is-ai-studio.md)
+- Learn more about what you can do in [Azure AI Foundry](../what-is-ai-foundry.md)
 - Get answers to frequently asked questions in the [Azure AI FAQ article](../faq.yml)
diff --git a/articles/ai-foundry/how-to/deploy-models-mistral.md b/articles/ai-foundry/how-to/deploy-models-mistral.md
@@ -2242,7 +2242,7 @@ For more information on how to track costs, see [Monitor costs for models offere
 
 * [Deploy models as serverless APIs](deploy-models-serverless.md)
 * [Azure AI Model Inference API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md)
-* [Model deprecation and retirement in Azure AI model catalog](../concepts/model-lifecycle-and-retirement.md)
+* [Model deprecation and retirement in Azure AI model catalog](/concepts/model-lifecycle-and-retirement.md)
 * [Consume serverless API endpoints from a different Azure AI Foundry project or hub](deploy-models-serverless-connect.md)
 * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)
 * [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)
diff --git a/articles/ai-foundry/how-to/deploy-models-openai.md b/articles/ai-foundry/how-to/deploy-models-openai.md
@@ -91,5 +91,5 @@ To learn more about quota, see [Azure AI Foundry quota](./quota.md) and [Manage
 
 ## Related content
 
-- Learn more about what you can do in [Azure AI Foundry](../what-is-ai-studio.md)
+- Learn more about what you can do in [Azure AI Foundry](../what-is-ai-foundry.md)
 - Get answers to frequently asked questions in the [Azure AI FAQ article](../faq.yml)
diff --git a/articles/ai-foundry/how-to/deploy-models-serverless-availability.md b/articles/ai-foundry/how-to/deploy-models-serverless-availability.md
@@ -39,7 +39,7 @@ Read [Consume serverless API endpoints from a different hub or project](deploy-m
 
 ## Related content
 
-- [Model Catalog and Collections](model-catalog.md)
+- [Model Catalog and Collections](model-catalog-overview.md)
 - [Deploy models as serverless API endpoints](deploy-models-serverless.md)
 
 
diff --git a/articles/ai-foundry/how-to/deploy-models-serverless-connect.md b/articles/ai-foundry/how-to/deploy-models-serverless-connect.md
@@ -239,5 +239,5 @@ Follow these steps to create a connection:
 
 ## Related content
 
-- [What is Azure AI Foundry?](../what-is-ai-studio.md)
+- [What is Azure AI Foundry?](../what-is-ai-foundry.md)
 - [Azure AI FAQ article](../faq.yml)
diff --git a/articles/ai-foundry/how-to/deploy-models-serverless.md b/articles/ai-foundry/how-to/deploy-models-serverless.md
@@ -31,7 +31,7 @@ This article uses a Meta Llama model deployment for illustration. However, you c
 
 - An [Azure AI Foundry project](create-projects.md).
 
-- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Foundry portal. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-studio.md).
+- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Foundry portal. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-foundry.md).
 
 - You need to install the following software to work with Azure AI Foundry:
 
@@ -687,7 +687,7 @@ Azure role-based access controls (Azure RBAC) are used to grant access to operat
   - `Microsoft.MachineLearningServices/workspaces/marketplaceModelSubscriptions/*`  
   - `Microsoft.MachineLearningServices/workspaces/serverlessEndpoints/*`
 
-For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-studio.md).
+For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-foundry.md).
 
 ## Related content
 
diff --git a/articles/ai-foundry/how-to/deploy-models-timegen-1.md b/articles/ai-foundry/how-to/deploy-models-timegen-1.md
@@ -38,7 +38,7 @@ You can deploy TimeGEN-1 as a serverless API with pay-as-you-go billing. Nixtla
 
 - An [Azure AI Foundry project](../how-to/create-projects.md).
 
-- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Foundry portal. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, visit [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-studio.md).
+- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Foundry portal. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, visit [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-foundry.md).
 
 
 #### Estimate the number of tokens needed
@@ -281,6 +281,6 @@ Quota is managed per deployment. Each deployment has a rate limit of 200,000 tok
 
 ## Related content
 
-- [What is Azure AI Foundry?](../what-is-ai-studio.md)
+- [What is Azure AI Foundry?](../what-is-ai-foundry.md)
 - [Azure AI FAQ article](../faq.yml)
 - [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)
diff --git a/articles/ai-foundry/how-to/deploy-stability-models.md b/articles/ai-foundry/how-to/deploy-stability-models.md
@@ -71,7 +71,7 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip
 
 ### Reference for Stability AI models deployed as a serverless API
 
-Stability AI models on Models as a Service implement the [Azure AI Model Inference API](../reference/reference-model-inference-api.md) on the route `/image/generations` 
+Stability AI models on Models as a Service implement the [Azure AI Model Inference API](../model-inference/reference/reference-model-inference-api.md) on the route `/image/generations` 
 
 #### Request example 
 
diff --git a/articles/ai-foundry/how-to/develop/connections-add-sdk.md b/articles/ai-foundry/how-to/develop/connections-add-sdk.md
@@ -34,7 +34,7 @@ Connections are a way to authenticate and consume both Microsoft and other resou
 
 ## Authenticating with Microsoft Entra ID
 
-There are various authentication methods for the different connection types. When you use Microsoft Entra ID, in addition to creating the connection you might also need to grant Azure role-based access control permissions before the connection can be used. For more information, visit [Role-based access control](../../concepts/rbac-ai-studio.md#scenario-connections-using-microsoft-entra-id-authentication).
+There are various authentication methods for the different connection types. When you use Microsoft Entra ID, in addition to creating the connection you might also need to grant Azure role-based access control permissions before the connection can be used. For more information, visit [Role-based access control](../../concepts/rbac-ai-foundry.md#scenario-connections-using-microsoft-entra-id-authentication).
 
 [!INCLUDE [Azure Key Vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/microsoft-entra-id-akv-expanded.md)]
 
diff --git a/articles/ai-foundry/how-to/develop/langchain.md b/articles/ai-foundry/how-to/develop/langchain.md
@@ -178,7 +178,7 @@ chain.invoke({"language": "italian", "text": "hi"})
 
 Models deployed to Azure AI Foundry support the Azure AI model inference API, which is standard across all the models. Chain multiple LLM operations based on the capabilities of each model so you can optimize for the right model based on capabilities. 
 
-In the following example, we create two model clients, one is a producer and another one is a verifier. To make the distinction clear, we are using a multi-model endpoint like the [Azure AI model inference service](../../ai-services/model-inference.md) and hence we are passing the parameter `model_name` to use a `Mistral-Large` and a `Mistral-Small` model, quoting the fact that **producing content is more complex than verifying it**.
+In the following example, we create two model clients, one is a producer and another one is a verifier. To make the distinction clear, we are using a multi-model endpoint like the [Azure AI model inference service](../../model-inference/overview.md) and hence we are passing the parameter `model_name` to use a `Mistral-Large` and a `Mistral-Small` model, quoting the fact that **producing content is more complex than verifying it**.
 
 ```python
 from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
@@ -466,5 +466,5 @@ Learn more about [how to visualize and manage traces](visualize-traces.md).
 
 * [Develop applications with LlamaIndex](llama-index.md)
 * [Visualize and manage traces in Azure AI Foundry](visualize-traces.md)
-* [Use the Azure AI model inference service](../../ai-services/model-inference.md)
+* [Use the Azure AI model inference service](../../model-inference/overview.md)
 * [Reference: Azure AI model inference API](../../../ai-foundry/model-inference/reference/reference-model-inference-api.md)
diff --git a/articles/ai-foundry/how-to/develop/llama-index.md b/articles/ai-foundry/how-to/develop/llama-index.md
@@ -88,7 +88,7 @@ llm = AzureAICompletionsModel(
 > [!TIP]
 > If your model deployment is hosted in Azure OpenAI service or Azure AI Services resource, configure the client as indicated at [Azure OpenAI models and Azure AI model inference service](#azure-openai-models-and-azure-ai-model-inference-service).
 
-If your endpoint is serving more than one model, like with the [Azure AI model inference service](../../ai-services/model-inference.md) or [GitHub Models](https://github.com/marketplace/models), you have to indicate `model_name` parameter:
+If your endpoint is serving more than one model, like with the [Azure AI model inference service](../../model-inference/overview.md) or [GitHub Models](https://github.com/marketplace/models), you have to indicate `model_name` parameter:
 
 ```python
 import os
@@ -133,9 +133,9 @@ llm = AzureAICompletionsModel(
 
 ### Azure OpenAI models and Azure AI model inference service
 
-If you are using Azure OpenAI service or [Azure AI model inference service](../../ai-services/model-inference.md), ensure you have at least version `0.2.4` of the LlamaIndex integration. Use `api_version` parameter in case you need to select a specific `api_version`. 
+If you are using Azure OpenAI service or [Azure AI model inference service](../../model-inference/overview.md), ensure you have at least version `0.2.4` of the LlamaIndex integration. Use `api_version` parameter in case you need to select a specific `api_version`. 
 
-For the [Azure AI model inference service](../../ai-services/model-inference.md), you need to pass `model_name` parameter:
+For the [Azure AI model inference service](../../model-inference/overview.md), you need to pass `model_name` parameter:
 
 ```python
 from llama_index.llms.azure_inference import AzureAICompletionsModel
diff --git a/articles/ai-foundry/how-to/develop/trace-production-sdk.md b/articles/ai-foundry/how-to/develop/trace-production-sdk.md
@@ -29,7 +29,7 @@ In this article, you learn to enable tracing, collect aggregated metrics, and co
 - The Azure CLI and the Azure Machine Learning extension to the Azure CLI. 
 - An Azure AI Foundry project. If you don't already have a project, you can [create one here](../../how-to/create-projects.md).
 - An Application Insights. If you don't already have an Application Insights resource, you can [create one here](/azure/azure-monitor/app/create-workspace-resource).
-- Azure role-based access controls are used to grant access to operations in Azure Machine Learning. To perform the steps in this article, you must have **Owner** or **Contributor** permissions on the selected resource group. For more information, see [Role-based access control in Azure AI Foundry portal](../../concepts/rbac-ai-studio.md).
+- Azure role-based access controls are used to grant access to operations in Azure Machine Learning. To perform the steps in this article, you must have **Owner** or **Contributor** permissions on the selected resource group. For more information, see [Role-based access control in Azure AI Foundry portal](../../concepts/rbac-ai-foundry.md).
 
 ## Deploy a flow for real-time inference
 
diff --git a/articles/ai-foundry/how-to/evaluate-generative-ai-app.md b/articles/ai-foundry/how-to/evaluate-generative-ai-app.md
@@ -250,5 +250,5 @@ Learn more about how to evaluate your generative AI applications:
 
 - [Evaluate your generative AI apps via the playground](./evaluate-prompts-playground.md)
 - [View the evaluation results](./evaluate-results.md)
-- Learn more about [harm mitigation techniques](../concepts/evaluation-improvement-strategies.md).
+- Learn more about [harm mitigation techniques](../concepts/evaluation-approach-gen-ai.md).
 - [Transparency Note for Azure AI Foundry safety evaluations](../concepts/safety-evaluations-transparency-note.md).
diff --git a/articles/ai-foundry/how-to/evaluate-prompts-playground.md b/articles/ai-foundry/how-to/evaluate-prompts-playground.md
@@ -83,4 +83,4 @@ Learn more about how to evaluate your generative AI applications:
 - [Evaluate your generative AI apps with the Azure AI Foundry portal or SDK](./evaluate-generative-ai-app.md)
 - [View the evaluation results](./evaluate-results.md)
 
-Learn more about [harm mitigation techniques](../concepts/evaluation-improvement-strategies.md).
+Learn more about [harm mitigation techniques](../concepts/evaluation-approach-gen-ai.md).
diff --git a/articles/ai-foundry/how-to/evaluate-results.md b/articles/ai-foundry/how-to/evaluate-results.md
@@ -171,4 +171,4 @@ Learn more about how to evaluate your generative AI applications:
 - [Evaluate your generative AI apps via the playground](../how-to/evaluate-prompts-playground.md)
 - [Evaluate your generative AI apps with the Azure AI Foundry portal or SDK](../how-to/evaluate-generative-ai-app.md)
 
-Learn more about [harm mitigation techniques](../concepts/evaluation-improvement-strategies.md).
+Learn more about [harm mitigation techniques](../concepts/evaluation-approach-gen-ai.md).
