name changes

Author: ssalgadodev

articles/ai-foundry/faq.yml

@@ -51,7 +51,7 @@ sections:
         answer: |
           Prompt templates in prompt flow provide robust examples and instructions for avoiding prompt injection attacks in the application. Azure AI Content Safety helps detect offensive or inappropriate content in text and images. Content moderation also checks for jailbreaks.
       - question: |
-          What is the billing model for Model-as-a-Service (MaaS)? 
+          What is the billing model for standard deployments? 
         answer: |
           Azure AI Foundry offers pay-as-you-go inference APIs and hosted fine-tuning for [Llama 2 family models](how-to/deploy-models-llama.md). Currently, there's no extra charge for Azure AI Foundry outside of typical AI services and other Azure resource charges.
       - question: |

articles/ai-foundry/how-to/built-in-policy-model-deployment.md

@@ -1,20 +1,20 @@
 ---
 title: Control AI model deployment with built-in policies
 titleSuffix: Azure AI Foundry
-description: "Learn how to use built-in Azure policies to control what managed AI Services (MaaS) and Model-as-a-Platform (MaaP) AI models can be deployed in Azure AI Foundry portal."
+description: "Learn how to use built-in Azure policies to control what managed AI Services (standard deployment) and Model-as-a-Platform (MaaP) AI models can be deployed in Azure AI Foundry portal."
 author: Blackmist
 ms.author: larryfr
 ms.service: azure-ai-foundry
 ms.topic: how-to #Don't change
 ms.date: 02/19/2025
 
-#customer intent: As an admin, I want control what Managed AI Services (MaaS) and Model-as-a-Platform (MaaP) AI models can be deployed by my developers.
+#customer intent: As an admin, I want control what Managed AI Services (standard deployment) and Model-as-a-Platform (MaaP) AI models can be deployed by my developers.
 
 ---
 
 # Control AI model deployment with built-in policies in Azure AI Foundry portal
 
-Azure Policy provides built-in policy definitions that help you govern the deployment of AI models in Managed AI Services (MaaS) and Model-as-a-Platform (MaaP). You can use these policies to control what models your developers can deploy in Azure AI Foundry portal.
+Azure Policy provides built-in policy definitions that help you govern the deployment of AI models in Managed AI Services (standard deployment) and Model-as-a-Platform (MaaP). You can use these policies to control what models your developers can deploy in Azure AI Foundry portal.
 
 ## Prerequisites
 

articles/ai-foundry/how-to/deploy-models-serverless.md

@@ -563,7 +563,7 @@ Read more about the [capabilities of this API](../../ai-foundry/model-inference/
 
 ## Network isolation
 
-Endpoints for models deployed as standard deployment follow the public network access (PNA) flag setting of the Azure AI Foundry portal Hub that has the project in which the deployment exists. To secure your MaaS endpoint, disable the PNA flag on your Azure AI Foundry Hub. You can secure inbound communication from a client to your endpoint by using a private endpoint for the hub.
+Endpoints for models deployed as standard deployment follow the public network access (PNA) flag setting of the Azure AI Foundry portal Hub that has the project in which the deployment exists. To secure your standard deployment, disable the PNA flag on your Azure AI Foundry Hub. You can secure inbound communication from a client to your endpoint by using a private endpoint for the hub.
 
 To set the PNA flag for the Azure AI Foundry hub:
 

articles/ai-foundry/how-to/evaluate-generative-ai-app.md

@@ -171,7 +171,7 @@ To create a new evaluation for your selected model deployment and defined prompt
 
 #### Basic information
 
-To start, you can set up the name for your evaluation run. Then select the **model deployment** you want to evaluate. We support both Azure OpenAI models and other open models compatible with Model-as-a-Service (MaaS), such as Meta Llama and Phi-3 family models. Optionally, you can adjust the model parameters like max response, temperature, and top P based on your need.  
+To start, you can set up the name for your evaluation run. Then select the **model deployment** you want to evaluate. We support both Azure OpenAI models and other open models compatible with standard deployment, such as Meta Llama and Phi-3 family models. Optionally, you can adjust the model parameters like max response, temperature, and top P based on your need.  
 
 In the System message text box, provide the prompt for your scenario. For more information on how to craft your prompt, see the prompt catalog. You can choose to add example to show the chat what responses you want. It will try to mimic any responses you add here to make sure they match the rules you laid out in the system message.
 

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

@@ -210,7 +210,7 @@ Here are some of the tasks you can do on the **Models** tab:
 
 ### Supported enterprise scenarios for fine-tuning
 
-Several enterprise scenarios are supported for MaaS fine-tuning. The table below outlines the supported configurations for user storage networking and authentication to ensure smooth operation within enterprise scenarios:
+Several enterprise scenarios are supported for standard deployment fine-tuning. 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 Foundry by clicking on the datastore connection which your dataset is stored in, and navigating to the **Access details** > **Authentication Method** setting.  
@@ -230,7 +230,7 @@ Several enterprise scenarios are supported for MaaS fine-tuning. The table below
 
 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 fine-tuning.
+Customer-Managed Keys (CMKs) is **not** a supported enterprise scenario with standard deployment fine-tuning.
 
 Issues fine-tuning with unique network setups on the workspace and storage usually points to a networking setup issue.
 
@@ -577,7 +577,7 @@ model_id = f"azureml://locations/{workspace.location}/workspaces/{workspace._wor
 
 ### Supported enterprise scenarios for fine-tuning
 
-Several enterprise scenarios are supported for MaaS fine-tuning. The table below outlines the supported configurations for user storage networking and authentication to ensure smooth operation within enterprise scenarios:
+Several enterprise scenarios are supported for standard deployment fine-tuning. 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 Foundry by clicking on the datastore connection which your dataset is stored in, and navigating to the **Access details** > **Authentication Method** setting.  
@@ -597,7 +597,7 @@ Several enterprise scenarios are supported for MaaS fine-tuning. The table below
 
 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 fine-tuning.
+Customer-Managed Keys (CMKs) is **not** a supported enterprise scenario with standard deployment fine-tuning.
 
 Issues fine-tuning with unique network setups on the workspace and storage usually points to a networking setup issue.
 

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

@@ -133,9 +133,9 @@ Models that are available for deployment as standard deployments with pay-as-you
 
 * Manages the hosting infrastructure.
 * Makes the inference APIs available.
-* Acts as the data processor for prompts submitted and content output by models deployed via MaaS.
+* Acts as the data processor for prompts submitted and content output by models deployed via standard deployment.
 
-Learn more about data processing for MaaS in the [article about data privacy](concept-data-privacy.md).
+Learn more about data processing for standard deployment in the [article about data privacy](concept-data-privacy.md).
 
 :::image type="content" source="../media/explore/model-publisher-cycle.png" alt-text="Diagram that shows the model publisher service cycle." lightbox="../media/explore/model-publisher-cycle.png":::
 
@@ -144,7 +144,7 @@ Learn more about data processing for MaaS in the [article about data privacy](co
 
 ### Billing
 
-The discovery, subscription, and consumption experience for models deployed via MaaS is in Azure AI Foundry portal and Azure Machine Learning studio. Users accept license terms for use of the models. Pricing information for consumption is provided during deployment.
+The discovery, subscription, and consumption experience for models deployed via standard deployment is in Azure AI Foundry portal and Azure Machine Learning studio. Users accept license terms for use of the models. Pricing information for consumption is provided during deployment.
 
 Models from non-Microsoft providers are billed through Azure Marketplace, in accordance with the [Microsoft Commercial Marketplace Terms of Use](/legal/marketplace/marketplace-terms).
 
@@ -182,7 +182,7 @@ To set the public network access flag for the Azure AI Foundry hub:
 
 * If you have an Azure AI Foundry hub with a private endpoint created before July 11, 2024, standard deployments added to projects in this hub won't follow the networking configuration of the hub. Instead, you need to create a new private endpoint for the hub and create new standard deployments in the project so that the new deployments can follow the hub's networking configuration.
 
-* If you have an Azure AI Foundry hub with MaaS deployments created before July 11, 2024, and you enable a private endpoint on this hub, the existing standard deployments won't follow the hub's networking configuration. For standard deployments in the hub to follow the hub's networking configuration, you need to create the deployments again.
+* If you have an Azure AI Foundry hub with standard deployment created before July 11, 2024, and you enable a private endpoint on this hub, the existing standard deployments won't follow the hub's networking configuration. For standard deployments in the hub to follow the hub's networking configuration, you need to create the deployments again.
 
 * Currently, [Azure OpenAI On Your Data](/azure/ai-services/openai/concepts/use-your-data) support isn't available for standard deployments in private hubs, because private hubs have the public network access flag disabled.
 

articles/machine-learning/concept-data-privacy.md

@@ -39,7 +39,7 @@ When you deploy a model from the model catalog (base or fine-tuned) as a standar
 
 [!INCLUDE [machine-learning-preview-generic-disclaimer](includes/machine-learning-preview-generic-disclaimer.md)]
 
-Microsoft acts as the data processor for prompts and outputs sent to and generated by a model deployed for pay-as-you-go inferencing (MaaS). Microsoft doesn't share these prompts and outputs with the model provider, and Microsoft doesn't use these prompts and outputs to train or improve Microsoft's, the model provider's, or any third party's models. Models are stateless and no prompts or outputs are stored in the model. If content filtering (preview) is enabled, prompts and outputs are screened for certain categories of harmful content by the Azure AI Content Safety service in real time; learn more about how Azure AI Content Safety processes data [here](/legal/cognitive-services/content-safety/data-privacy). Prompts and outputs are processed within the geography specified during deployment but may be processed between regions within the geography for operational purposes (including performance and capacity management).
+Microsoft acts as the data processor for prompts and outputs sent to and generated by a model deployed for standard deployment. Microsoft doesn't share these prompts and outputs with the model provider, and Microsoft doesn't use these prompts and outputs to train or improve Microsoft's, the model provider's, or any third party's models. Models are stateless and no prompts or outputs are stored in the model. If content filtering (preview) is enabled, prompts and outputs are screened for certain categories of harmful content by the Azure AI Content Safety service in real time; learn more about how Azure AI Content Safety processes data [here](/legal/cognitive-services/content-safety/data-privacy). Prompts and outputs are processed within the geography specified during deployment but may be processed between regions within the geography for operational purposes (including performance and capacity management).
 
 :::image type="content" source="media/concept-data-privacy/model-publisher-cycle.png" alt-text="A diagram showing model publisher service cycle." lightbox="media/concept-data-privacy/model-publisher-cycle.png":::