how-to folder

Author: msakande

.openpublishing.redirection.json

@@ -349,6 +349,101 @@
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 }

articles/ai-foundry/model-inference/how-to/configure-content-filters.md renamed to articles/ai-foundry/foundry-models/how-to/configure-content-filters.md

@@ -18,18 +18,18 @@ reviewer: santiagxf
 # How to configure content filters for models in Azure AI Foundry
 
 ::: zone pivot="ai-foundry-portal"
-[!INCLUDE [portal](../includes/configure-content-filters/portal.md)]
+[!INCLUDE [portal](../../model-inference/includes/configure-content-filters/portal.md)]
 ::: zone-end
 
 ::: zone pivot="programming-language-cli"
-[!INCLUDE [cli](../includes/configure-content-filters/cli.md)]
+[!INCLUDE [cli](../../model-inference/includes/configure-content-filters/cli.md)]
 ::: zone-end
 
 ::: zone pivot="programming-language-bicep"
-[!INCLUDE [bicep](../includes/configure-content-filters/bicep.md)]
+[!INCLUDE [bicep](../../model-inference/includes/configure-content-filters/bicep.md)]
 ::: zone-end
 
 ## Next steps
 
-- Read more about [content filtering categories and severity levels](../concepts/content-filter.md) with Azure OpenAI in Azure AI Foundry Models.
+- Read more about [content filtering categories and severity levels](../../model-inference/concepts/content-filter.md) with Azure OpenAI in Azure AI Foundry Models.
 - Learn more about red teaming from our: [Introduction to red teaming large language models (LLMs) article](../../../ai-services/openai/concepts/red-teaming.md).

articles/ai-foundry/model-inference/how-to/configure-deployment-policies.md renamed to articles/ai-foundry/foundry-models/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 Foundry (formerly known Azure AI Services) and Azure OpenAI with [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs), 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 Foundry (formerly known Azure AI Services) and Azure OpenAI with [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs), you might need to use custom policies to control which [type of deployment](../../model-inference/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. 
 
 > [!TIP]
 > The steps in this article apply to both a [!INCLUDE [fdp](../../includes/fdp-project-name.md)] and [!INCLUDE [hub](../../includes/hub-project-name.md)].
@@ -183,5 +183,5 @@ To update an existing policy assignment with new models, follow these steps:
 ## Related content
 
 - [Azure Policy overview](/azure/governance/policy/overview)
-- [Deployment types](../concepts/deployment-types.md)
+- [Deployment types](../../model-inference/concepts/deployment-types.md)
 

articles/ai-foundry/model-inference/how-to/configure-entra-id.md renamed to articles/ai-foundry/foundry-models/how-to/configure-entra-id.md

@@ -18,17 +18,17 @@ reviewer: santiagxf
 # Configure key-less authentication with Microsoft Entra ID
 
 ::: zone pivot="ai-foundry-portal"
-[!INCLUDE [portal](../includes/configure-entra-id/portal.md)]
+[!INCLUDE [portal](../../model-inference/includes/configure-entra-id/portal.md)]
 ::: zone-end
 
 ::: zone pivot="programming-language-cli"
-[!INCLUDE [cli](../includes/configure-entra-id/cli.md)]
+[!INCLUDE [cli](../../model-inference/includes/configure-entra-id/cli.md)]
 ::: zone-end
 
 ::: zone pivot="programming-language-bicep"
-[!INCLUDE [bicep](../includes/configure-entra-id/bicep.md)]
+[!INCLUDE [bicep](../../model-inference/includes/configure-entra-id/bicep.md)]
 ::: zone-end
 
 ## Next steps
 
-* [Develop applications using Azure AI Foundry Models](../supported-languages.md)
+* [Develop applications using Azure AI Foundry Models](../../model-inference/supported-languages.md)

articles/ai-foundry/model-inference/how-to/configure-marketplace.md renamed to articles/ai-foundry/foundry-models/how-to/configure-marketplace.md

@@ -20,7 +20,7 @@ Certain models in Azure AI Foundry Models are offered directly by the model prov
 
 :::image type="content" source="../media/configure-marketplace/azure-marketplace-3p.png" alt-text="A diagram with the overall architecture of Azure Marketplace integration with AI Foundry Models." lightbox="../media/configure-marketplace/azure-marketplace-3p.png":::
 
-[!INCLUDE [marketplace-rbac](../includes/configure-marketplace/rbac.md)]
+[!INCLUDE [marketplace-rbac](../../model-inference/includes/configure-marketplace/rbac.md)]
 
 ## Country availability
 

articles/ai-foundry/model-inference/how-to/configure-project-connection.md renamed to articles/ai-foundry/foundry-models/how-to/configure-project-connection.md

@@ -18,17 +18,17 @@ reviewer: santiagxf
 # Configure a connection to use Azure AI Foundry Models in your AI project
 
 ::: zone pivot="ai-foundry-portal"
-[!INCLUDE [portal](../includes/configure-project-connection/portal.md)]
+[!INCLUDE [portal](../../model-inference/includes/configure-project-connection/portal.md)]
 ::: zone-end
 
 ::: zone pivot="programming-language-cli"
-[!INCLUDE [cli](../includes/configure-project-connection/cli.md)]
+[!INCLUDE [cli](../../model-inference/includes/configure-project-connection/cli.md)]
 ::: zone-end
 
 ::: zone pivot="programming-language-bicep"
-[!INCLUDE [bicep](../includes/configure-project-connection/bicep.md)]
+[!INCLUDE [bicep](../../model-inference/includes/configure-project-connection/bicep.md)]
 ::: zone-end
 
 ## Next steps
 
-* [Develop applications using Azure AI Foundry Models](../supported-languages.md)
+* [Develop applications using Azure AI Foundry Models](../../model-inference/supported-languages.md)

articles/ai-foundry/model-inference/how-to/create-model-deployments.md renamed to articles/ai-foundry/foundry-models/how-to/create-model-deployments.md

@@ -18,17 +18,17 @@ reviewer: santiagxf
 # Add and configure models to Azure AI Foundry Models
 
 ::: zone pivot="ai-foundry-portal"
-[!INCLUDE [portal](../includes/create-model-deployments/portal.md)]
+[!INCLUDE [portal](../../model-inference/includes/create-model-deployments/portal.md)]
 ::: zone-end
 
 ::: zone pivot="programming-language-cli"
-[!INCLUDE [cli](../includes/create-model-deployments/cli.md)]
+[!INCLUDE [cli](../../model-inference/includes/create-model-deployments/cli.md)]
 ::: zone-end
 
 ::: zone pivot="programming-language-bicep"
-[!INCLUDE [bicep](../includes/create-model-deployments/bicep.md)]
+[!INCLUDE [bicep](../../model-inference/includes/create-model-deployments/bicep.md)]
 ::: zone-end
 
 ## Next steps
 
-* [Develop applications using Azure AI Foundry Models](../supported-languages.md)
+* [Develop applications using Azure AI Foundry Models](../../model-inference/supported-languages.md)

articles/ai-foundry/model-inference/how-to/inference.md renamed to articles/ai-foundry/foundry-models/how-to/inference.md

@@ -14,11 +14,11 @@ ms.reviewer: fasantia
 
 # Use Foundry Models
 
-Once you have [deployed a model in Azure AI Foundry](create-model-deployments.md), you can consume its capabilities via Azure AI Foundry APIs. There are two different endpoints and APIs to use models in Azure AI Foundry Models.
+Once you have [deployed a model in Azure AI Foundry](../../model-inference/how-to/create-model-deployments.md), you can consume its capabilities via Azure AI Foundry APIs. There are two different endpoints and APIs to use models in Azure AI Foundry Models.
 
 ## Models inference endpoint
 
-The models inference endpoint (usually with the form `https://<resource-name>.services.ai.azure.com/models`) allows customers to use a single endpoint with the same authentication and schema to generate inference for the deployed models in the resource. This endpoint follows the [Azure AI Model Inference API](.././reference/reference-model-inference-api.md) which all the models in Foundry Models support. It supports the following modalities:
+The models inference endpoint (usually with the form `https://<resource-name>.services.ai.azure.com/models`) allows customers to use a single endpoint with the same authentication and schema to generate inference for the deployed models in the resource. This endpoint follows the [Azure AI Model Inference API](../../model-inference/reference/reference-model-inference-api.md) which all the models in Foundry Models support. It supports the following modalities:
 
 * Text embeddings
 * Image embeddings
@@ -32,9 +32,9 @@ The inference endpoint routes requests to a given deployment by matching the par
 
 For example, if you create a deployment named `Mistral-large`, then such deployment can be invoked as:
 
-[!INCLUDE [code-create-chat-client](../includes/code-create-chat-client.md)]
+[!INCLUDE [code-create-chat-client](../../model-inference/includes/code-create-chat-client.md)]
 
-[!INCLUDE [code-create-chat-completion](../includes/code-create-chat-completion.md)]
+[!INCLUDE [code-create-chat-completion](../../model-inference/includes/code-create-chat-completion.md)]
 
 > [!TIP]
 > Deployment routing isn't case sensitive.
@@ -50,12 +50,12 @@ Azure OpenAI endpoints (usually with the form `https://<resource-name>.openai.az
 
 Each deployment has a URL that is the concatenations of the **Azure OpenAI** base URL and the route `/deployments/<model-deployment-name>`.
 
-[!INCLUDE [code-create-openai-client](../includes/code-create-openai-client.md)]
+[!INCLUDE [code-create-openai-client](../../model-inference/includes/code-create-openai-client.md)]
 
-[!INCLUDE [code-create-openai-chat-completion](../includes/code-create-openai-chat-completion.md)]
+[!INCLUDE [code-create-openai-chat-completion](../../model-inference/includes/code-create-openai-chat-completion.md)]
 
 
 ## Next steps
 
-* [Use embedding models](use-embeddings.md)
-* [Use chat completion models](use-chat-completions.md)
+* [Use embedding models](../../model-inference/how-to/use-embeddings.md)
+* [Use chat completion models](../../model-inference/how-to/use-chat-completions.md)

articles/ai-foundry/model-inference/how-to/manage-costs.md renamed to articles/ai-foundry/foundry-models/how-to/manage-costs.md

@@ -13,7 +13,7 @@ reviewer: santiagxf
 
 # Monitor model deployments in Azure AI Foundry Models
 
-[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+[!INCLUDE [Feature preview](../../model-inference/includes/feature-preview.md)]
 
 When you have critical applications and business processes that rely on Azure resources, you need to monitor and get alerts for your system. The Azure Monitor service collects and aggregates metrics and logs from every component of your system, including Foundry Models deployments. You can use this information to view availability, performance, and resilience, and get notifications of issues.
 
@@ -23,10 +23,10 @@ This document explains how you can use metrics and logs to monitor model deploym
 
 To use monitoring capabilities for model deployments in Foundry Models, you need the following:
 
-* An Azure AI services resource. For more information, see [Create an Azure AI Services resource](quickstart-create-resources.md).
+* An Azure AI services resource. For more information, see [Create an Azure AI Services resource](../../model-inference/how-to/quickstart-create-resources.md).
 
     > [!TIP]
-    > If you are using Serverless API Endpoints and you want to take advantage of monitoring capabilities explained in this document, [migrate your Serverless API Endpoints to Foundry Models](quickstart-ai-project.md).
+    > If you are using Serverless API Endpoints and you want to take advantage of monitoring capabilities explained in this document, [migrate your Serverless API Endpoints to Foundry Models](../../model-inference/how-to/quickstart-ai-project.md).
 
 * At least one model deployment.