Fix merge conflicts

Author: ShawnKupfer

.openpublishing.redirection.json

@@ -5460,3 +5460,88 @@ achievements:
   title: Secure the Altair emulator
   summary: Learn how to help secure the Altair emulator network communications.
   iconUrl: /training/achievements/student-evangelism/altair-azure-sphere-secure-emulator.svg
+- uid: learn.student-evangelism.altair-azure-sphere-program-emulator.badge
+  type: badge
+  title: Program the Altair 8800 emulator
+  summary: Learn how to program the Altair 8800 emulator.
+  iconUrl: /training/achievements/student-evangelism/altair-azure-sphere-program-emulator.svg
+- uid: learn.student-evangelism.altair-azure-sphere-introduction.badge
+  type: badge
+  title: Introduction to the Altair 8800 and Azure Sphere
+  summary: Introduction to computing fundamentals with Altair 8800 and Azure Sphere.
+  iconUrl: /training/achievements/student-evangelism/altair-azure-sphere-introduction.svg
+- uid: learn.student-evangelism.altair-azure-sphere-deploy-mqtt-broker.badge
+  type: badge
+  title: Deploy a private MQTT broker
+  summary: Learn how to deploy your own private MQTT broker.
+  iconUrl: /training/achievements/student-evangelism/altair-azure-sphere-deploy-mqtt-broker.svg
+- uid: learn.student-evangelism.altair-azure-sphere-deploy-emulator.badge
+  type: badge
+  title: Deploy the Altair emulator to Azure Sphere
+  summary: Learn how to deploy the Altair 8800 emulator to Azure Sphere.
+  iconUrl: /training/achievements/student-evangelism/altair-azure-sphere-deploy-emulator.svg
+- uid: learn.altair-azure-sphere-computing-fundamentals.trophy
+  type: trophy
+  title: Learn computing fundamentals with Altair 8800 and Azure Sphere
+  summary: In this learning path, learn computing fundamentals by creating a cloud-enabled Altair 8800 on Azure Sphere solution.
+  iconUrl: /training/achievements/student-evangelism/altair-azure-sphere-computing-fundamentals.svg
+- uid: learn.student-evangelism.altair-azure-sphere-create-iot-central-application.badge
+  type: badge
+  title: Create an Azure IoT Central application for the Altair emulator and Azure Sphere
+  summary: Learn how to create a cloud-based Azure IoT Central application for the Altair 8800 emulator and Azure Sphere.
+  iconUrl: /training/achievements/student-evangelism/altair-azure-sphere-create-iot-central-application.svg
+- uid: learn.student-evangelism.altair-azure-sphere-create-web-terminal.badge
+  type: badge
+  title: Create and customize Altair 8800 cloud services
+  summary: Learn how to customize IoT Central, create an Altair web terminal, and run the Altair virtual disk server.
+  iconUrl: /training/achievements/student-evangelism/altair-azure-sphere-create-web-terminal.svg
+- uid: learn.student-evangelism.health-bot-template-scenarios.badge
+  type: badge
+  title: Azure Health Bot scenario templates
+  summary: Learn how to enrich the capabilities of your bot by using the prebuilt scenario templates.
+  iconUrl: /training/achievements/student-evangelism/health-bot-template-scenarios.svg
+- uid: learn.student-evangelism.health-bot-language-understanding.badge
+  type: badge
+  title: Language understanding in Azure Health Bot
+  summary: Language understanding plays a fundamental role in the working of Azure Health Bot.
+  iconUrl: /training/achievements/student-evangelism/health-bot-language-understanding.svg
+- uid: learn.student-evangelism.health-bot-introduction.badge
+  type: badge
+  title: Introduction to Azure Health Bot
+  summary: Azure Health Bot enables users to build a health bot by using built-in scenarios or custom ones. The information that each instance of the Health Bot service handles is privacy protected to HIPAA standards. It also meets Microsoft's high standards for privacy and security.
+  iconUrl: /training/achievements/student-evangelism/health-bot-introduction.svg
+- uid: learn.student-evangelism.health-bot-integrated-bot.badge
+  type: badge
+  title: Integrate Azure Health Bot with a database
+  summary: Learn how to integrate an advanced bot with a database.
+  iconUrl: /training/achievements/student-evangelism/health-bot-integrated-bot.svg
+- uid: learn.student-evangelism.health-bot-enhanced.badge
+  type: badge
+  title: Enhanced Azure Health Bot
+  summary: In the previous module, you created a basic informative bot without any interrupting or breaking scenario involved. This module shows you how to enhance the bot with advanced functionalities.
+  iconUrl: /training/achievements/student-evangelism/health-bot-enhanced.svg
+- uid: learn.student-evangelism.health-bot-channelized-bot.badge
+  type: badge
+  title: Channelized Azure Health Bot
+  summary: In the previous module, you integrated a bot with a database hosted on Azure. This module shows you how to make the bot available on Teams and build a basic app.
+  iconUrl: /training/achievements/student-evangelism/health-bot-channelized-bot.svg
+- uid: learn.student-evangelism.health-bot-case-studies.badge
+  type: badge
+  title: Azure Health Bot case studies
+  summary: The Health Bot service is a cloud platform that empowers developers in healthcare organizations to build and deploy compliant, AI-powered virtual health assistants and health bots that help them improve processes and reduce costs.
+  iconUrl: /training/achievements/student-evangelism/health-bot-case-studies.svg
+- uid: learn.student-evangelism.health-bot-built-in-scenarios.badge
+  type: badge
+  title: Azure Health Bot built-in scenarios
+  summary: Azure Health Bot supports many built-in scenarios. Examples include triaging a medical condition, finding information about a disease or types of drugs, and getting user consent.
+  iconUrl: /training/achievements/student-evangelism/health-bot-built-in-scenarios.svg
+- uid: learn.create-bots-azure-health-bot.trophy
+  type: trophy
+  title: Create intelligent health bots with Azure Health Bot
+  summary: Azure Health Bot enables users to build a health bot by using built-in or custom scenarios. The information that each instance of the Health Bot service handles is privacy protected to HIPAA standards. It also meets Microsoft's high standards for privacy and security. The modules in this Learning path guide you through the creation of both a basic and enhanced health bot. You'll learn about language understanding and how to use both built-in and template scenarios.
+  iconUrl: /training/achievements/create-bots-azure-health-bot.svg
+- uid: learn.student-evangelism.health-bot-basic-bot.badge
+  type: badge
+  title: Basic Azure Health Bot
+  summary: This module starts with a basic informative bot that has no interrupting or breaking scenario involved. It shows you how to enhance the bot with more advanced functionalities.
+  iconUrl: /training/achievements/student-evangelism/health-bot-basic-bot.svg

learn-pr/achievements.yml

@@ -4,7 +4,7 @@ title: Azure OpenAI Service overview
 metadata:
   title: Azure OpenAI Service overview
   description: Provide an overview of Azure OpenAI Services.
-  ms.date: 10/24/2024
+  ms.date: 03/13/2025
   author: rmcmurray
   ms.author: robmcm
   ms.topic: unit

learn-pr/azure/optimize-spend-performance-azure-openai-service-provisioned-reservations/azure-openai-service-overview.yml

@@ -4,7 +4,7 @@ title: Choose and purchase the right Azure OpenAI Service provisioned reservatio
 metadata:
   title: Choose and purchase the right Azure OpenAI Service provisioned reservation
   description: Describe how to estimate, request, and deploy provisioned throughput units (PTUs) in Azure OpenAI Service.
-  ms.date: 10/24/2024
+  ms.date: 03/13/2025
   author: rmcmurray
   ms.author: robmcm
   ms.topic: unit

learn-pr/azure/optimize-spend-performance-azure-openai-service-provisioned-reservations/choose-purchase-azure-openai-service-provisioned-reservation.yml

@@ -4,7 +4,7 @@ title: Estimate, request, and deploy provisioned throughput units
 metadata:
   title: Estimate, request, and deploy provisioned throughput units
   description: Describe how to estimate, request, and deploy PTUs in Azure OpenAI Service.
-  ms.date: 10/24/2024
+  ms.date: 03/13/2025
   author: rmcmurray
   ms.author: robmcm
   ms.topic: unit

learn-pr/azure/optimize-spend-performance-azure-openai-service-provisioned-reservations/estimate-request-deploy-provisioned-throughput-units.yml

@@ -2,7 +2,7 @@
 
 Azure OpenAI Service is a result of the partnership between Microsoft and OpenAI. The service combines Azure's enterprise-grade capabilities with OpenAI's generative AI model capabilities. With Azure OpenAI, customers get the security capabilities of Azure while running the same models as OpenAI. Azure OpenAI offers private networking, regional availability, and responsible AI content filtering.
 
-Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including the o1 series, GPT-4o series, and other legacy models such as GPT-3.5 Turbo. You can easily adapt these models to your specific task, including—but not limited to—content generation, summarization, image understanding, semantic search, and natural language-to-code translation. Users can access the service through REST APIs, Python SDK, or a web-based interface in the Azure OpenAI Studio.
+Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including the o1 series, GPT-4o series, and other legacy models such as GPT-3.5 Turbo. You can easily adapt these models to your specific task, including—but not limited to—content generation, summarization, image understanding, semantic search, and natural language-to-code translation. Users can access the service through REST APIs, Python SDK, or a web-based interface in the Azure OpenAI Foundry.
 
 Azure OpenAI Service is powered by a diverse set of models with different capabilities and price points. Model availability varies by region and cloud service. Azure OpenAI Service gives customers advanced language AI with OpenAI GPT-4, GPT-3, Codex, DALL-E, Whisper, and text-to-speech models augmented with the security and enterprise features of Azure. Azure OpenAI co-develops the application program interfaces (APIs) with OpenAI, which helps ensure compatibility and a smooth transition from one product to the other.
 
@@ -27,7 +27,7 @@ A high-detail image lets the API review the image in more detail by cropping it
 
 ## How to start with Azure OpenAI
 
-Similar to other Azure products, you can start with Azure OpenAI by creating a resource or an instance of the service in your Azure Subscription. Once you create an Azure OpenAI Resource, you must deploy a model before you can start making API calls and generating text. You can do this by using Deployment APIs, which allows you to specify the model you wish to use. In the Azure OpenAI Studio, you can build AI models and deploy them for public consumption in software applications.
+Similar to other Azure products, you can start with Azure OpenAI by creating a resource or an instance of the service in your Azure Subscription. Once you create an Azure OpenAI Resource, you must deploy a model before you can start making API calls and generating text. You can do this by using Deployment APIs, which allows you to specify the model you wish to use. In the Azure OpenAI Foundry, you can build AI models and deploy them for public consumption in software applications.
 
 ## Summary
 

learn-pr/azure/optimize-spend-performance-azure-openai-service-provisioned-reservations/includes/azure-openai-service-overview.md

@@ -2,7 +2,7 @@ When you start with Azure OpenAI, we recommend that you use the Standard deploym
 
 In general, prompt tokens are less expensive to process than an equal number of generated tokens. This also means that the distribution of these call shapes is important in overall throughput. Traffic patterns with a wide distribution that include some very large calls might experience lower throughput per PTU than a narrower distribution with the same average prompt and completion token sizes.
 
-## Using the Capacity Calculator in Azure OpenAI Studio
+## Using the Capacity Calculator in Azure OpenAI Foundry
 
 Determining the right number of PTUs you require for your workload is an essential step to optimizing performance and cost. You can use the Azure OpenAI capacity calculator to help estimate the required number of PTUs to meet the needs of your workload.
 
@@ -50,21 +50,21 @@ Creating a new deployment requires an available (unused) quota to cover the desi
 
 In this scenario, 200 PTUs of quota are considered used, and 300 PTUs are available for use to create new deployments.
 
-A default number of provisioned and global provisioned quota is assigned to all subscriptions in several regions. You can review the quota available to you in a region by visiting the Quotas blade in Azure OpenAI Studio and selecting the desired subscription and region.
+A default number of provisioned and global provisioned quota is assigned to all subscriptions in several regions. You can review the quota available to you in a region by visiting the Quotas blade in Azure OpenAI Foundry and selecting the desired subscription and region.
 
 For example, the following screenshot displays a quota limit of 500 PTUs in West US for the selected subscription. Note that you might observe lower values of available default quotas.
 
-:::image type="content" source="../media/4-quota-limit-small.png" alt-text="A screenshot of the available quotas in Azure OpenAI Studio." border="true" lightbox="../media/4-quota-limit.png":::
+:::image type="content" source="../media/4-quota-limit-small.png" alt-text="A screenshot of the available quotas in Azure OpenAI Foundry." border="true" lightbox="../media/4-quota-limit.png":::
 
-By default, PTU quota is available in many regions. If an additional quota is required, customers can request it by using the **Request Quota** link next to the **Provisioned Managed Throughput Unit** quota item in Azure OpenAI Studio. The form allows customers to request an increase in the PTU quota for a specified region. After the request is approved, customers will receive an email at the included address, typically within two business days.
+By default, PTU quota is available in many regions. If an additional quota is required, customers can request it by using the **Request Quota** link next to the **Provisioned Managed Throughput Unit** quota item in Azure OpenAI Foundry. The form allows customers to request an increase in the PTU quota for a specified region. After the request is approved, customers will receive an email at the included address, typically within two business days.
 
 ## Creating a provisioned deployment - capacity is available
 
 You can create PTUs by using Azure OpenAI resource objects within Azure. You must have an Azure OpenAI resource in each region where you intend to create a deployment. Use the Azure portal to create a resource in a region with an available quota, if required. Note that Azure OpenAI resources can support multiple types of Azure OpenAI deployments at the same time. It isn't necessary to dedicate new resources for your provisioned deployments.
 
 To create a provisioned deployment, follow the instructions in the **Deploy model** dialog box, entering the required information as depicted in the following example.
 
-:::image type="content" source="../media/4-deploy-model-dialog-box.png" alt-text="A screenshot of the Azure OpenAI Studio deployment page for a provisioned deployment." border="true":::
+:::image type="content" source="../media/4-deploy-model-dialog-box.png" alt-text="A screenshot of the Azure OpenAI Foundry deployment page for a provisioned deployment." border="true":::
 
 Important things to note when creating a provisioned deployment:
 
@@ -82,9 +82,9 @@ The following image displays an example of the pricing confirmation that you can
 
 Due to the dynamic nature of capacity availability, it is possible that the region of your selected resource might not have the service capacity to create the deployment of the specified model, version, and number of PTUs.
 
-In this event, Azure OpenAI Studio will direct you to other regions with available quota and capacity to create a deployment of the desired model. If this happens, the **Deploy model** dialog box might display information as depicted in the following screenshot.
+In this event, Azure OpenAI Foundry will direct you to other regions with available quota and capacity to create a deployment of the desired model. If this happens, the **Deploy model** dialog box might display information as depicted in the following screenshot.
 
-:::image type="content" source="../media/4-deploy-model-capacity-not-available.png" alt-text="A screenshot of the Azure OpenAI Studio deployment page for a provisioned deployment with no capacity available." border="true":::
+:::image type="content" source="../media/4-deploy-model-capacity-not-available.png" alt-text="A screenshot of the Azure OpenAI Foundry deployment page for a provisioned deployment with no capacity available." border="true":::
 
 Important things to note:
 

learn-pr/azure/optimize-spend-performance-azure-openai-service-provisioned-reservations/includes/estimate-request-deploy-provisioned-throughput-units.md

@@ -4,7 +4,7 @@ Microsoft Azure OpenAI services include provisioned deployments, also known as p
 
 Contoso Retail, a mid-sized retail company, wants to enhance customer support by implementing an AI-powered chatbot that can manage common customer inquiries, provide product recommendations, and assist with order tracking. Contoso Retail wants to design its chatbot to be highly available, have low latency, and provide predictable performance while managing costs effectively. For these reasons, they've decided to use Azure OpenAI as the core engine for their chatbot. You work as an Azure administrator for Contoso Retail. You've been tasked to recommend an appropriate solution to allocate resources for chatbot deployment in Azure OpenAI.
 
-## What will we be doing?
+## What will we learn?
 
 In this module, you'll learn how to efficiently allocate and use resources needed for Azure OpenAI Service deployment. You'll review the various Azure OpenAI deployment models and understand how to request and deploy PTUs. You'll also learn how to select and purchase the appropriate Azure OpenAI Service provisioned reservation based on your organization's requirements. Finally, you'll examine how to manage and monitor the provisioned reservations.
 

learn-pr/azure/optimize-spend-performance-azure-openai-service-provisioned-reservations/includes/introduction.md

@@ -12,7 +12,7 @@ You implemented the following process to resolve Contoso's requirements:
 
    Contoso estimates they'll need 100 PTUs: GPT -4os in US West.
 
-3. [Check PTU quota](/azure/ai-services/openai/how-to/provisioned-get-started) based on chosen region in Azure OpenAI Studio:
+3. [Check PTU quota](/azure/ai-services/openai/how-to/provisioned-get-started) based on chosen region in Azure OpenAI Foundry:
 
    Contoso confirms 100 PTUs of PGT -4os are available in US West.