Merge branch 'main' into release-2025-openai-march-latest

Author: v-alje

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

@@ -4,10 +4,6 @@ titleSuffix: Azure AI Foundry
 description: Learn about deploying models in Azure AI Foundry portal.
 manager: scottpolly
 ms.service: azure-ai-foundry
-ms.custom:
-  - ignite-2023
-  - build-2024
-  - ignite-2024
 ms.topic: concept-article
 ms.date: 10/21/2024
 ms.reviewer: fasantia
@@ -17,22 +13,28 @@ author: msakande
 
 # Overview: Deploy AI models in Azure AI Foundry portal
 
-The model catalog in Azure AI Foundry portal is the hub to discover and use a wide range of models for building generative AI applications. Models need to be deployed to make them available for receiving inference requests. The process of interacting with a deployed model is called *inferencing*. Azure AI Foundry offer a comprehensive suite of deployment options for those models depending on your needs and model requirements.
+The model catalog in Azure AI Foundry portal is the hub to discover and use a wide range of models for building generative AI applications. Models need to be deployed to make them available for receiving inference requests. Azure AI Foundry offers a comprehensive suite of deployment options for those models depending on your needs and model requirements.
 
 ## Deploying models
 
-Deployment options vary depending on the model type:
+Deployment options vary depending on the model offering:
 
-* **Azure OpenAI models:** The latest OpenAI models that have enterprise features from Azure.
-* **Models as a Service models:** These models don't require compute quota from your subscription. This option allows you to deploy your Model as a Service (MaaS). You use a serverless API deployment and are billed per token in a pay-as-you-go fashion.
-* **Open and custom models:** The model catalog offers access to a large variety of models across modalities that are of open access. You can host open models in your own subscription with a managed infrastructure, virtual machines, and the number of instances for capacity management. There's a wide range of models from Azure OpenAI, Hugging Face, and NVIDIA.
+* **Azure OpenAI models:** The latest OpenAI models that have enterprise features from Azure with flexible billing options.
+* **Models-as-a-Service models:** These models don't require compute quota from your subscription and are billed per token in a pay-as-you-go fashion. 
+* **Open and custom models:** The model catalog offers access to a large variety of models across modalities, including models of open access. You can host open models in your own subscription with a managed infrastructure, virtual machines, and the number of instances for capacity management.
 
 Azure AI Foundry offers four different deployment options:
 
 |Name                           | Azure OpenAI service | Azure AI model inference | Serverless API | Managed compute |
 |-------------------------------|----------------------|-------------------|----------------|-----------------|
-| Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Models as a Service](../../ai-foundry/model-inference/concepts/models.md) | [Models as a Service](../how-to/model-catalog-overview.md#content-safety-for-models-deployed-via-serverless-apis) | [Open and custom models](../how-to/model-catalog-overview.md#availability-of-models-for-deployment-as-managed-compute) |
+| Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Models-as-a-Service](../../ai-foundry/model-inference/concepts/models.md) | [Models-as-a-Service](../how-to/model-catalog-overview.md#content-safety-for-models-deployed-via-serverless-apis) | [Open and custom models](../how-to/model-catalog-overview.md#availability-of-models-for-deployment-as-managed-compute) |
 | Deployment resource           | Azure OpenAI resource | Azure AI services resource | AI project resource | AI project resource |
+| Requires Hubs/Projects        | No | No | Yes | Yes |
+| Data processing options       | Regional <br /> Data-zone  <br /> Global | Global | Regional | Regional |
+| Private networking            | Yes | Yes | Yes | Yes |
+| Content filtering             | Yes | Yes | Yes | No  |
+| Custom content filtering      | Yes | Yes | No  | No  |
+| Key-less authentication       | Yes | Yes | No  | No  |
 | Best suited when              | You are planning to use only OpenAI models | You are planning to take advantage of the flagship models in Azure AI catalog, including OpenAI. | You are planning to use a single model from a specific provider (excluding OpenAI). | If you plan to use open models and you have enough compute quota available in your subscription. |
 | Billing bases                 | Token usage & PTU         | Token usage       | Token usage<sup>1</sup>      | Compute core hours<sup>2</sup> |
 | Deployment instructions       | [Deploy to Azure OpenAI Service](../how-to/deploy-models-openai.md) | [Deploy to Azure AI model inference](../model-inference/how-to/create-model-deployments.md) | [Deploy to Serverless API](../how-to/deploy-models-serverless.md) | [Deploy to Managed compute](../how-to/deploy-models-managed.md) |
@@ -48,18 +50,16 @@ Azure AI Foundry offers four different deployment options:
 
 Azure AI Foundry encourages customers to explore the deployment options and pick the one that best suites their business and technical needs. In general you can use the following thinking process:
 
-1. Start with the deployment options that have the bigger scopes. This allows you to iterate and prototype faster in your application without having to rebuild your architecture each time you decide to change something. [Azure AI model inference](../../ai-foundry/model-inference/overview.md) is a deployment target that supports all the flagship models in the Azure AI catalog, including latest innovation from Azure OpenAI. To get started, follow [Configure your AI project to use Azure AI model inference](../../ai-foundry/model-inference/how-to/quickstart-ai-project.md).
+* Start with [Azure AI model inference](../../ai-foundry/model-inference/overview.md) which is the option with the bigger scope. This allows you to iterate and prototype faster in your application without having to rebuild your architecture each time you decide to change something. If you are using Azure AI Foundry Hubs or Projects, enable it by [turning on Azure AI model inference](../../ai-foundry/model-inference/how-to/quickstart-ai-project.md).
 
-2. When you are looking to use a specific model:
+* When you are looking to use a specific model:
 
-   1. When you are interested in Azure OpenAI models, use the Azure OpenAI Service which offers a wide range of capabilities for them and it's designed for them.
+   * When you are interested in Azure OpenAI models, use the Azure OpenAI Service which offers a wide range of capabilities for them and it's designed for them.
 
-   2. When you are interested in a particular model from Models as a Service, and you don't expect to use any other type of model, use [Serverless API endpoints](../how-to/deploy-models-serverless.md). They allow deployment of a single model under a unique set of endpoint URL and keys.
+   * When you are interested in a particular model from Models-as-a-Service, and you don't expect to use any other type of model, use [Serverless API endpoints](../how-to/deploy-models-serverless.md). They allow deployment of a single model under a unique set of endpoint URL and keys.
 
-3. When your model is not available in Models as a Service and you have compute quota available in your subscription, use [Managed Compute](../how-to/deploy-models-managed.md) which support deployment of open and custom models. It also allows high level of customization of the deployment inference server, protocols, and detailed configuration.
+* When your model is not available in Models-as-a-Service and you have compute quota available in your subscription, use [Managed Compute](../how-to/deploy-models-managed.md) which support deployment of open and custom models. It also allows high level of customization of the deployment inference server, protocols, and detailed configuration.
 
-> [!TIP]
-> Each deployment option may offer different capabilities in terms of networking, security, and additional features like content safety. Review the documentation for each of them to understand their limitations.
 
 ## Related content
 

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

@@ -145,7 +145,7 @@ After you select and upload the training dataset, select **Next** to continue.
 The next step provides options to configure the model to use validation data in the training process. If you don't want to use validation data, you can choose **Next** to continue to the advanced options for the model. Otherwise, if you have a validation dataset, you can either choose existing prepared validation data or upload new prepared validation data to use when customizing your model.
 The **Validation data** pane displays any existing, previously uploaded training and validation datasets and provides options by which you can upload new validation data.
 
-### Automatic Split of Training Data
+### Split training data
 You can automatically divide your training data to generate a validation dataset.
 After you select Automatic split of training data, select **Next** to continue.
 
@@ -213,7 +213,7 @@ Here are some of the tasks you can do on the **Models** tab:
 Several enterprise scenarios are supported for MaaS finetuning. The table below outlines the supported configurations for user storage networking and authentication to ensure smooth operation within enterprise scenarios:
 
 >[!Note]  
->- Data connections auth can be changed via AI Studio by clicking on the datastore connection which your dataset is stored in, and navigating to the **Access details** > **Authentication Method** setting.  
+>- Data connections auth can be changed via AI Foundry by clicking on the datastore connection which your dataset is stored in, and navigating to the **Access details** > **Authentication Method** setting.  
 >- Storage auth can be changed in Azure Storage > **Settings** > **Configurations** page > **Allow storage account key access**.  
 >- Storage networking can be changed in Azure Storage > **Networking** page.
 
@@ -228,7 +228,7 @@ Several enterprise scenarios are supported for MaaS finetuning. The table below
 | Public Network Access = Disabled                              | Account key disabled           | Entra-Based Auth (Credentialless) | Yes, UX and SDK. <br><br> *Note:* for UX data upload and submission to work, the workspace _needs to be accessed from within the Vnet_ that has appropriate access to the storage                |
 
 
-The scenarios above should work in a Managed Vnet workspace as well. See setup of Managed Vnet AI Studio hub here: [How to configure a managed network for Azure AI Foundry hubs](./configure-managed-network.md)
+The scenarios above should work in a Managed Vnet workspace as well. See setup of Managed Vnet AI Foundry hub here: [How to configure a managed network for Azure AI Foundry hubs](./configure-managed-network.md)
 
 Customer-Managed Keys (CMKs) is **not** a supported enterprise scenario with MaaS finetuning.
 
@@ -240,12 +240,10 @@ Issues finetuning with unique network setups on the workspace and storage usuall
 When the fine-tuning job succeeds, you can deploy the custom model from the **Fine-tune** tab. You must deploy your custom model to make it available for use with completion calls.
 
 > [!IMPORTANT]
-> After you deploy a customized model, if at any time the deployment remains inactive for greater than fifteen (15) days, the deployment is deleted. The deployment of a
-> customized model is inactive if the model was deployed more than fifteen (15) days ago and no completions or chat completions calls were made to it during a continuous 15
-> day period.
-> The deletion of an inactive deployment doesn't delete or affect the underlying customized model, and the customized model can be redeployed at any time. As described in
-> Azure AI Foundry pricing, each customized (fine-tuned) model that's deployed incurs an hourly hosting cost regardless of whether completions or chat completions calls are
-> being made to the model. To learn more about planning and managing costs with Azure AI Foundry, refer to the guidance in [Plan to manage costs for Azure AI Foundry Service](./costs-plan-manage.md).  
+> After you deploy a customized model and finishing with the endpoint, please remember to clean up any inactive endpoints. The deletion of an inactive deployment doesn't
+> delete or affect the underlying customized model, and the customized model can be redeployed at any time. As described in Azure AI Foundry pricing, each customized (fine-
+> tuned) model that's deployed incurs an hourly hosting cost regardless of whether completions or chat completions calls are being made to the model. To learn more about
+> planning and managing costs with Azure AI Foundry, refer to the guidance in [Plan to manage costs for Azure AI Foundry Service](./costs-plan-manage.md).  
 
 > [!NOTE]
 > Only one deployment is permitted for a custom model. An error message is displayed if you select an already-deployed custom model.
@@ -297,7 +295,7 @@ For more information on how to track costs, see [Monitor costs for models offere
 
 :::image type="content" source="../media/deploy-monitor/serverless/costs-model-as-service-cost-details.png" alt-text="A screenshot showing different resources corresponding to different model offers and their associated meters." lightbox="../media/deploy-monitor/serverless/costs-model-as-service-cost-details.png":::
 
-## Sample Notebook
+## Sample notebook
 
 You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/model-as-a-service/chat-completion/chat_completion_with_model_as_service.ipynb) to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than what's used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
 

articles/ai-services/document-intelligence/authentication/encrypt-data-at-rest.md

@@ -6,27 +6,30 @@ author: erindormier
 manager: venkyv
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 11/19/2024
+ms.date: 03/19/2025
 monikerRange: '<=doc-intel-4.0.0'
 ---
 
 
-# Document Intelligence encryption of data at rest
+# Encrypt data at rest
 
 [!INCLUDE [applies to v4.0, v3.1, v3.0, and v2.1](../includes/applies-to-v40-v31-v30-v21.md)]
 
 > [!IMPORTANT]
 >
-> * Earlier versions of customer managed keys only encrypted your models.
-> *Starting with the  ```07/31/2023``` release, all new resources use customer managed keys to encrypt both the models and document results.
-> To upgrade an existing service to encrypt both the models and the data, simply disable and reenable the customer managed key.
+> * Earlier versions of customer managed keys (`CMK`) only encrypted your models.
+> * Beginning with the  ```07/31/2023``` release, all new resources utilize customer-managed keys to encrypt both models and document results.
+> * [Delete analyze response](/rest/api/aiservices/document-models/delete-analyze-result?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true&tabs=HTTP). the `analyze response` is stored for 24 hours from when the operation completes for retrieval. For scenarios where you want to delete the response sooner, use the delete analyze response API to delete the response.  
+> * To upgrade an existing service to encrypt both the models and the data, disable and reenable the customer managed key.
 
-Azure AI Document Intelligence automatically encrypts your data when persisting it to the cloud. Document Intelligence encryption protects your data to help you to meet your organizational security and compliance commitments.
+Azure AI Document Intelligence automatically encrypts your data when persisting it to the cloud. Document Intelligence encryption protects your data to help you to meet your organizational security and compliance commitments.  
 
 [!INCLUDE [cognitive-services-about-encryption](../../../ai-services/includes/cognitive-services-about-encryption.md)]
 
 > [!IMPORTANT]
-> Customer-managed keys are only available resources created after 11 May, 2020. To use CMK with Document Intelligence, you will need to create a new Document Intelligence resource. Once the resource is created, you can use Azure Key Vault to set up your managed identity.
+> * Customer-managed keys are only available resources created after May 11, 2020. To use customer-managed keys with Document Intelligence, you need to create a new Document Intelligence resource. Once the resource is created, you can use Azure Key Vault to set up your managed identity.
+> * The scope for data encrypted with customer-managed keys includes the `analysis response` stored for 24 hours, allowing the operation results to be retrieved during that 24-hour time period.
+
 
 [!INCLUDE [cognitive-services-cmk](../../../ai-services/includes/configure-customer-managed-keys.md)]
 

articles/ai-services/document-intelligence/tutorial/azure-function.md

@@ -7,7 +7,7 @@ manager: nitinme
 
 ms.service: azure-ai-document-intelligence
 ms.topic: tutorial
-ms.date: 11/19/2024
+ms.date: 03/19/2025
 ms.author: lajanuar
 ms.custom: VS Code-azure-extension-update-completed, devx-track-python
 ---