Merge pull request #5724 from msakande/fix-merge-conflict2

[Dirty PR] Fix merge conflict2

Author: v-ccolin

articles/ai-foundry/agents/concepts/model-region-support.md

@@ -27,7 +27,7 @@ All deployments can perform the exact same inference operations, however the bil
 Azure AI Foundry Agent Service supports the following Azure OpenAI models in the listed regions.
 
 > [!NOTE]
-> * The following table is for standard deployment availability. For information on Provisioned Throughput Unit (PTU) availability, see [provisioned throughput](../../../ai-services/openai/concepts/provisioned-throughput.md) in the Azure OpenAI documentation. `GlobalStandard` customers also have access to [global standard models](../../../ai-services/openai/concepts/models.md#global-standard-model-availability). 
+> * The following table is for serverless API deployment availability. For information on Provisioned Throughput Unit (PTU) availability, see [provisioned throughput](../../../ai-services/openai/concepts/provisioned-throughput.md) in the Azure OpenAI documentation. `GlobalStandard` customers also have access to [global standard models](../../../ai-services/openai/concepts/models.md#global-standard-model-availability). 
 > * [Hub based projects](../../what-is-azure-ai-foundry.md#project-types) are limited to the following models: gpt-4o, gpt-4o-mini, gpt-4, gpt-35-turbo
 
 | REGION           | o1 | o3-mini | gpt-4.1, 2025-04-14 | gpt-4.1-mini, 2025-04-14 | gpt-4.1-nano, 2025-04-14 | gpt-4o, 2024-05-13 | gpt-4o, 2024-08-06 | gpt-4o, 2024-11-20 | gpt-4o-mini, 2024-07-18 | gpt-4, 0613 | gpt-4, turbo-2024-04-09 | gpt-4-32k, 0613 | gpt-35-turbo, 1106 | gpt-35-turbo, 0125 |

articles/ai-foundry/agents/includes/quickstart-python.md

@@ -65,7 +65,6 @@ project_endpoint = os.environ["PROJECT_ENDPOINT"]  # Ensure the PROJECT_ENDPOINT
 project_client = AIProjectClient(
     endpoint=project_endpoint,
     credential=DefaultAzureCredential(),  # Use Azure Default Credential for authentication
-    api_version="latest",
 )
 
 code_interpreter = CodeInterpreterTool()

articles/ai-foundry/concepts/architecture.md

@@ -35,7 +35,7 @@ At the top level, Azure AI Foundry provides access to the following resources:
     For more information, visit [Management center](management-center.md).
 - **Azure AI Foundry hub**: The hub is the top-level resource in Azure AI Foundry portal, and is based on the Azure Machine Learning service. The Azure resource provider for a hub is `Microsoft.MachineLearningServices/workspaces`, and the kind of resource is `Hub`. It provides the following features:
     - Security configuration including a managed network that spans projects and model endpoints.
-    - Compute resources for interactive development, fine-tuning, open source, and standard deployment for models.
+    - Compute resources for interactive development, fine-tuning, open source, and serverless API deployment for models.
     - Connections to other Azure services such as Azure OpenAI, Azure AI services, and Azure AI Search. Hub-scoped connections are shared with projects created from the hub.
     - Project management. A hub can have multiple child projects.
     - An associated Azure storage account for data upload and artifact storage.

articles/ai-foundry/concepts/content-filtering.md

@@ -26,7 +26,7 @@ author: PatrickFarley
 
 The content filtering system is powered by [Azure AI Content Safety](../../ai-services/content-safety/overview.md), and it works by running both the model prompt input and completion output through a set of classification models designed to detect and prevent the output of harmful content. Variations in API configurations and application design might affect completions and thus filtering behavior.
 
-With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later). Models available through **standard deployments** have content filtering enabled by default. To learn more about the default content filter enabled for standard deployments, see [Content safety for Models Sold Directly by Azure ](model-catalog-content-safety.md).
+With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later). Models available through **serverless API deployments** have content filtering enabled by default. To learn more about the default content filter enabled for serverless API deployments, see [Content safety for Models Sold Directly by Azure ](model-catalog-content-safety.md).
 
 ## Language support
 

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

@@ -20,14 +20,14 @@ The model catalog in Azure AI Foundry portal is the hub to discover and use a wi
 Deployment options vary depending on the model offering:
 
 * **Azure OpenAI in Azure AI Foundry Models:** The latest OpenAI models that have enterprise features from Azure with flexible billing options.
-* **Standard deployment:** These models don't require compute quota from your subscription and are billed per token in a serverless pay per token offer. 
+* **Serverless API deployment:** These models don't require compute quota from your subscription and are billed per token in a serverless API deployment. 
 * **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 | Azure AI Foundry Models | Standard deployment | Managed compute |
+|Name                           | Azure OpenAI | Azure AI Foundry Models | Serverless API deployment | Managed compute |
 |-------------------------------|----------------------|-------------------|----------------|-----------------|
-| Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Standard deployment](../../ai-foundry/model-inference/concepts/models.md) | [Standard deployment](../how-to/model-catalog-overview.md) | [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 serverless API deployment](../../ai-foundry/model-inference/concepts/models.md) | [serverless API deployment](../how-to/model-catalog-overview.md) | [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 |
@@ -37,9 +37,9 @@ Azure AI Foundry offers four different deployment options:
 | Key-less authentication       | Yes | Yes | No  | No  |
 | Best suited when              | You're planning to use only OpenAI models | You're planning to take advantage of the flagship models in Azure AI catalog, including OpenAI. | You're 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 & [provisioned throughput units](../../ai-services/openai/concepts/provisioned-throughput.md)        | Token usage       | Token usage<sup>1</sup>      | Compute core hours<sup>2</sup> |
-| Deployment instructions       | [Deploy to Azure OpenAI](../how-to/deploy-models-openai.md) | [Deploy to Foundry Models](../model-inference/how-to/create-model-deployments.md) | [Deploy to Standard deployment](../how-to/deploy-models-serverless.md) | [Deploy to Managed compute](../how-to/deploy-models-managed.md) |
+| Deployment instructions       | [Deploy to Azure OpenAI](../how-to/deploy-models-openai.md) | [Deploy to Foundry Models](../model-inference/how-to/create-model-deployments.md) | [Deploy to serverless API deployment](../how-to/deploy-models-serverless.md) | [Deploy to Managed compute](../how-to/deploy-models-managed.md) |
 
-<sup>1</sup> A minimal endpoint infrastructure is billed per minute. You aren't billed for the infrastructure that hosts the model in standard deployment. After you delete the endpoint, no further charges accrue.
+<sup>1</sup> A minimal endpoint infrastructure is billed per minute. You aren't billed for the infrastructure that hosts the model in serverless API deployment. After you delete the endpoint, no further charges accrue.
 
 <sup>2</sup> Billing is on a per-minute basis, depending on the product tier and the number of instances used in the deployment since the moment of creation. After you delete the endpoint, no further charges accrue.
 
@@ -56,9 +56,9 @@ Azure AI Foundry encourages you to explore various deployment options and choose
 
    * If you're interested in Azure OpenAI models, use Azure OpenAI in Foundry Models. This option is designed for Azure OpenAI models and offers a wide range of capabilities for them.
 
-   * If you're interested in a particular model from serverless pay per token offer, and you don't expect to use any other type of model, use [Standard deployment](../how-to/deploy-models-serverless.md). Standard deployments allow deployment of a single model under a unique set of endpoint URL and keys.
+   * If you're interested in a particular model from serverless pay per token offer, and you don't expect to use any other type of model, use [serverless API deployment](../how-to/deploy-models-serverless.md). serverless API deployments allow deployment of a single model under a unique set of endpoint URL and keys.
 
-* When your model isn't available in standard deployment and you have compute quota available in your subscription, use [Managed Compute](../how-to/deploy-models-managed.md), which supports deployment of open and custom models. It also allows a high level of customization of the deployment inference server, protocols, and detailed configuration.
+* When your model isn't available in serverless API deployment and you have compute quota available in your subscription, use [Managed Compute](../how-to/deploy-models-managed.md), which supports deployment of open and custom models. It also allows a high level of customization of the deployment inference server, protocols, and detailed configuration.
 
 
 ## Related content

articles/ai-foundry/concepts/fine-tuning-overview.md

@@ -96,7 +96,7 @@ To find steps to fine-tuning a model in AI Foundry, see [Fine-tune Models in AI
 
 Now that you know when to use fine-tuning for your use case, you can go to Azure AI Foundry to find models available to fine-tune.
 
-**To fine-tune an AI Foundry model using Serverless** you must have a hub/project in the region where the model is available for fine tuning. See [Region availability for models in standard deployment](../how-to/deploy-models-serverless-availability.md) for detailed information on model and region availability, and [How to Create a Hub based project](../how-to/create-projects.md) to create your project.
+**To fine-tune an AI Foundry model using Serverless** you must have a hub/project in the region where the model is available for fine tuning. See [Region availability for models in serverless API deployment](../how-to/deploy-models-serverless-availability.md) for detailed information on model and region availability, and [How to Create a Hub based project](../how-to/create-projects.md) to create your project.
 
 **To fine-tune an OpenAI model** you can use an Azure OpenAI Resource, a Foundry resource or default project, or a hub/project. GPT 4.1, 4.1-mini and 4.1-nano are available in all regions with Global Training. For regional availability, see [Regional Availability and Limits for Azure OpenAI Fine Tuning](../../ai-services/openai/concepts/models.md). See [Create a project for Azure AI Foundry](../how-to/create-projects.md) for instructions on creating a new project.
 
@@ -107,4 +107,4 @@ Now that you know when to use fine-tuning for your use case, you can go to Azure
 
 - [Fine-tune models using managed compute (preview)](../how-to/fine-tune-managed-compute.md)
 - [Fine-tune an Azure OpenAI model in Azure AI Foundry portal](../../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-studio/context/context)
-- [Fine-tune models using standard deployment](../how-to/fine-tune-serverless.md)
+- [Fine-tune models using serverless API deployment](../how-to/fine-tune-serverless.md)