fixing links

Author: Blackmist

articles/ai-foundry/.openpublishing.redirection.ai-studio.json

@@ -76,7 +76,7 @@
             "redirect_document_id": true
         },
         {
-            "source_path_from_root": "/articles/ai-studio/concepts/modele-lifecycle-retirement.md",
+            "source_path_from_root": "/articles/ai-studio/concepts/model-lifecycle-retirement.md",
             "redirect_url": "/azure/ai-foundry/concepts/model-lifecycle-retirement",
             "redirect_document_id": true
         },

articles/ai-foundry/concepts/deployments-overview.md

@@ -66,5 +66,5 @@ Azure AI Foundry encourages customers to explore the deployment options and pick
 * [Configure your AI project to use Azure AI model inference](../../ai-foundry/model-inference/how-to/quickstart-ai-project.md)
 * [Add and configure models to Azure AI model inference](../model-inference/how-to/create-model-deployments.md)
 * [Deploy Azure OpenAI models with Azure AI Foundry](../how-to/deploy-models-openai.md)
-* [Deploy open models with Azure AI Foundry](../how-to/deploy-models-open.md)
+* [Deploy open models with Azure AI Foundry](../how-to/deploy-models-managed.md)
 * [Model catalog and collections in Azure AI Foundry portal](../how-to/model-catalog-overview.md)

articles/ai-foundry/how-to/deploy-models-timegen-1.md

@@ -18,7 +18,7 @@ ms.custom: references_regions, build-2024, ignite-2024
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
 In this article, you learn how to use Azure AI Foundry to deploy the TimeGEN-1 model as a serverless API with pay-as-you-go billing.
-You filter on the Nixtla collection to browse the TimeGEN-1 model in the [Model Catalog](model-catalog.md).
+You filter on the Nixtla collection to browse the TimeGEN-1 model in the [Model Catalog](model-catalog-overview.md).
 
 The Nixtla TimeGEN-1 is a generative, pretrained forecasting and anomaly detection model for time series data. TimeGEN-1 can produce accurate forecasts for new time series without training, using only historical values and exogenous covariates as inputs.
 

articles/ai-foundry/how-to/fine-tune-managed-compute.md

@@ -32,7 +32,7 @@ To improve model performance, you might consider fine-tuning a foundation model
 
 - 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 __owner__ or __contributor__ role for the Azure subscription. 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 __owner__ or __contributor__ role for the Azure subscription. For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-foundry.md).
 
 ## Fine-tune a foundation model using managed compute
 

articles/ai-foundry/how-to/fine-tune-serverless.md

@@ -32,7 +32,7 @@ In this article, you learn how to fine-tune models that are deployed via serverl
 
 - 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 __owner__ or __contributor__ role for the Azure subscription. 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 __owner__ or __contributor__ role for the Azure subscription. For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-ai-foundry.md).
 
 
 ## Find models with fine-tuning support
@@ -256,6 +256,6 @@ Models deployed as a service with pay-as-you-go billing are protected by Azure A
 
 
 ## Next steps
-- [What is Azure AI Foundry?](../what-is-ai-studio.md)
+- [What is Azure AI Foundry?](../what-is-ai-foundry.md)
 - [Learn more about deploying Mistral models](./deploy-models-mistral.md)
 - [Azure AI FAQ article](../faq.yml)

articles/ai-foundry/how-to/flow-deploy.md

@@ -79,7 +79,7 @@ Follow the steps below to deploy a prompt flow as an online endpoint in Azure AI
 For more information, see the sections below.
 
 > [!TIP]
-> For a guide about how to deploy a base model, see [Deploying models with Azure AI Foundry](deploy-models-open.md).
+> For a guide about how to deploy a base model, see [Deploying models with Azure AI Foundry](deploy-models-managed.md).
 
 ## Settings and configurations
 
@@ -246,6 +246,6 @@ If you aren't going use the endpoint after completing this tutorial, you should
 
 ## 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)
 - [Enable trace and collect feedback for your deployment](./develop/trace-production-sdk.md)

articles/ai-foundry/how-to/index-add.md

@@ -85,4 +85,4 @@ You must have:
 ## Related content
 
 - [Learn more about RAG](../concepts/retrieval-augmented-generation.md)
-- [Build and consume an index using code](./develop/index-build-consume-sdk.md)
+- [Build and consume an index using code](../tutorials/copilot-sdk-create-resources.md)

articles/ai-foundry/how-to/model-catalog-overview.md

@@ -33,7 +33,7 @@ The model catalog organizes models into different collections:
 
 * **Azure OpenAI models exclusively available on Azure**: Flagship Azure OpenAI models available through an integration with Azure OpenAI Service. Microsoft supports these models and their use according to the product terms and [SLA for Azure OpenAI Service](https://www.microsoft.com/licensing/docs/view/Service-Level-Agreements-SLA-for-Online-Services).
 
-* **Open models from the Hugging Face hub**: Hundreds of models from the Hugging Face hub for real-time inference with managed compute. Hugging Face creates and maintains models listed in this collection. For help, use the [Hugging Face forum](https://discuss.huggingface.co) or [Hugging Face support](https://huggingface.co/support). Learn more in [Deploy open models with Azure AI Foundry](deploy-models-open.md).
+* **Open models from the Hugging Face hub**: Hundreds of models from the Hugging Face hub for real-time inference with managed compute. Hugging Face creates and maintains models listed in this collection. For help, use the [Hugging Face forum](https://discuss.huggingface.co) or [Hugging Face support](https://huggingface.co/support). Learn more in [Deploy open models with Azure AI Foundry](deploy-models-managed.md).
 
 You can submit a request to add a model to the model catalog by using [this form](https://forms.office.com/pages/responsepage.aspx?id=v4j5cvGGr0GRqy180BHbR_frVPkg_MhOoQxyrjmm7ZJUM09WNktBMURLSktOWEdDODBDRjg2NExKUy4u).
 
@@ -97,7 +97,7 @@ Nixtla | Not available | TimeGEN-1
 AI models evolve fast, and when a new version or a new model with updated capabilities in the same model family become available, older models may be retired in the AI Foundry model catalog. To allow for a smooth transition to a newer model version, some models provide users with the option to enable automatic updates. To learn more about the model lifecycle of different models, upcoming model retirement dates, and suggested replacement models and versions, see:
 
 - [Azure OpenAI Service model deprecations and retirements](../../ai-services/openai/concepts/model-retirements.md)
-- [Serverless API model deprecations and retirements](../../ai-studio/concepts/model-lifecycle-retirement.md)
+- [Serverless API model deprecations and retirements](../concepts/model-lifecycle-retirement.md)
 
 ## Managed compute
 
@@ -117,12 +117,12 @@ The registries build on top of a highly scalable and enterprise-ready infrastruc
 
 ### Deployment of models for inference with managed compute
 
-Models available for deployment to managed compute can be deployed to Azure Machine Learning managed compute for real-time inference. Deploying to managed compute requires you to have a virtual machine quota in your Azure subscription for the specific products that you need to optimally run the model. Some models allow you to deploy to a [temporarily shared quota for model testing](deploy-models-open.md).
+Models available for deployment to managed compute can be deployed to Azure Machine Learning managed compute for real-time inference. Deploying to managed compute requires you to have a virtual machine quota in your Azure subscription for the specific products that you need to optimally run the model. Some models allow you to deploy to a [temporarily shared quota for model testing](deploy-models-managed.md).
 
 Learn more about deploying models:
 
 * [Deploy Meta Llama models](deploy-models-llama.md)
-* [Deploy Azure AI Foundry open models](deploy-models-open.md)
+* [Deploy Azure AI Foundry open models](deploy-models-managed.md)
 
 ### Building generative AI apps with managed compute
 
@@ -199,5 +199,5 @@ To set the public network access flag for the Azure AI Foundry hub:
 
 ## Related content
 
-* [Explore foundation models in Azure AI Foundry portal](models-foundation-azure-ai.md)
-* [Model deprecation and retirement in Azure AI model catalog](../concepts/model-lifecycle-and-retirement.md)
+* [Explore foundation models in Azure AI Foundry portal](../ai-services/how-to/connect-ai-services.md)
+* [Model deprecation and retirement in Azure AI model catalog](../concepts/model-lifecycle-retirement.md)

articles/ai-foundry/how-to/monitor-quality-safety.md

@@ -48,7 +48,7 @@ Before following the steps in this article, make sure you have the following pre
 
 - A prompt flow ready for deployment. If you don't have one, see [Develop a prompt flow](flow-develop.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).
 
 # [Python SDK](#tab/python)
 
@@ -539,5 +539,5 @@ After you create your monitor from the SDK, you can [consume the monitoring resu
 
 ## 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).

articles/ai-foundry/how-to/secure-data-playground.md

@@ -216,7 +216,7 @@ For more information on assigning roles, see [Tutorial: Grant a user access to r
 
 ## Use your data in Azure AI Foundry portal  
 
-Now, the data you add to Azure AI Foundry is secured to the isolated network provided by your Azure AI Foundry hub and project. For an example of using data, visit the [build a question and answer copilot](../tutorials/deploy-copilot-ai-studio.md) tutorial.
+Now, the data you add to Azure AI Foundry is secured to the isolated network provided by your Azure AI Foundry hub and project. For an example of using data, visit the [build a question and answer copilot](../tutorials/deploy-copilot-ai-foundry.md) tutorial.
 
 ## Deploy web apps