pull base content,head:wwlpublishsync,into:5a44f90d167093ecd22070d686dea88d4b17f12c19b76e6cf52de193449512be-live

Author: wwlpublish

.openpublishing.redirection.json

@@ -647,6 +647,21 @@
       "redirect_url": "https://learn.microsoft.com/training/modules/introduction-language/",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/learn-pr/wwl-data-ai/fundamentals-machine-learning/10-exercise-auto-machine-learning.yml",
+      "redirect_url": "https://learn.microsoft.com/training/modules/fundamentals-machine-learning/",
+      "redirect_document_id": false
+    },
+	  {
+      "source_path_from_root": "/learn-pr/wwl-data-ai/fundamentals-generative-ai/6b-quality-responses.yml",
+      "redirect_url": "https://learn.microsoft.com/training/modules/fundamentals-generative-ai/",
+      "redirect_document_id": false
+    },
+	  {
+      "source_path_from_root": "/learn-pr/wwl-data-ai/fundamentals-generative-ai/7-exercise.yml",
+      "redirect_url": "https://learn.microsoft.com/training/modules/fundamentals-generative-ai/",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/learn-pr/wwl-data-ai/explore-azure-openai/1-introduction.yml",
       "redirect_url": "https://learn.microsoft.com/training/modules/introduction-to-azure-ai-studio/",

learn-pr/wwl-data-ai/ai-information-extraction/index.yml

@@ -1,7 +1,7 @@
 ### YamlMime:Module
 uid: learn.wwl.ai-information-extraction
 metadata:
-  title: Get started with AI-powered information extraction in Azure
+  title: Get started with AI-powered information extraction on Azure
   description: AI gives you the power to unlock insights from your data. In this module, you'll learn how to use Azure AI services to extract information from content.
   author: graememalcolm
   ms.author: gmalc
@@ -11,7 +11,7 @@ metadata:
   ms.topic: module-standard-task-based
   ms.collection:
     - wwl-ai-copilot
-title: Get started with AI-powered information extraction in Azure
+title: Get started with AI-powered information extraction on Azure
 summary: AI gives you the power to unlock insights from your data. In this module, you'll learn how to use Azure AI services to extract information from content.
 abstract: |
   After completing this module, you'll be able to:

learn-pr/wwl-data-ai/fundamentals-azure-ai-services/includes/1-introduction.md

@@ -1,6 +1,6 @@
 Artificial Intelligence (AI) is changing our world and there’s hardly an industry that hasn't been affected. From better healthcare to online safety, AI is helping us to tackle some of society’s biggest issues. 
 
-Azure AI services are a portfolio of AI capabilities that unlock automation for workloads in language, vision, intelligent search, content generation, and much more. They are straightforward to implement and don’t require specialist AI knowledge.
+Azure AI services are a portfolio of AI capabilities that unlock automation for workloads in language, vision, information extraction, content generation, and much more. They are straightforward to implement and don’t require specialist AI knowledge.
 
 Organizations are using Azure AI services in innovative ways, such as within [robots](https://customers.microsoft.com/story/1615185041460958543-intuition-robotics-consumer-goods-azure-text-to-speech?azure-portal=true) to provide life-like companionship to older people by expressing happiness, concern, and even laughter. In other use cases, scientists are using AI to protect [endangered species](https://news.microsoft.com/features/artificial-intelligence-makes-a-splash-in-efforts-to-protect-alaskas-ice-seals-and-beluga-whales-2?azure-portal=true) by identifying hard-to-find animals in images. This was previously time-consuming and error-prone work, which the Azure AI Vision service can complete quickly and with a high degree of accuracy, freeing scientists to do other work. 
 

learn-pr/wwl-data-ai/fundamentals-azure-ai-services/includes/3-create-azure-ai-resource.md

@@ -9,7 +9,7 @@ You can create a resource [several ways](/azure/developer/intro/azure-developer-
 
 ## How to use the Azure portal to create an Azure AI services resource 
 
-To create an Azure AI services resource, sign in to the [Azure portal](https://portal.azure.com?azure-portal=true) with Contributor access and select **Create a resource**. To create a multi-services resource search for *Azure AI services* in the marketplace. 
+To create an Azure AI services resource, sign in to the [Azure portal](https://portal.azure.com?azure-portal=true). To create a multi-services resource search for *Azure AI services* in the marketplace. 
 
 ![Screenshot of Azure AI services in the Azure portal marketplace.](../media/azure-ai-services-marketplace.png)
 

learn-pr/wwl-data-ai/fundamentals-azure-ai-services/index.yml

@@ -1,7 +1,7 @@
 ### YamlMime:Module
 uid: learn.wwl.fundamentals-azure-ai-services
 metadata:
-  title: Fundamentals of Azure AI services
+  title: Get started with Azure AI services
   description: "This module is an introduction to how Azure AI services can be used to build applications."
   ms.date: 04/25/2025
   author: wwlpublish
@@ -12,7 +12,7 @@ metadata:
   - N/A
   ms.service: azure-ai-services
   ai-usage: human-only
-title: Fundamentals of Azure AI services
+title: Get started with Azure AI services
 summary: In this module, you learn the fundamentals of how Azure AI services can be used to build applications.
 abstract: |
   -   Understand applications Azure AI services can be used to build

learn-pr/wwl-data-ai/fundamentals-generative-ai/6b-quality-responses.yml

@@ -38,17 +38,6 @@ quiz:
     - content: "Large Language Models have fewer parameters than Small Language Models."
       isCorrect: false
       explanation: "Incorrect. Large Language Models have many billions (even trillions) of parameters, which is more than Small Language Models."
-  - content: "What is the purpose of fine-tuning in the context of generative AI?"
-    choices:
-    - content: "It's used to manage access, authentication, and data usage in AI models."
-      isCorrect: false
-      explanation: "This statement describes the role of security and governance controls, not fine-tuning."
-    - content: "It involves connecting a language model to an organization's proprietary database."
-      isCorrect: false
-      explanation: "This statement describes the function of Retrieval-Augmented Generation, not fine-tuning."
-    - content: "It involves further training a pretrained model on a task-specific dataset to make it more suitable for a particular application."
-      isCorrect: true 
-      explanation: "Fine-tuning allows the model to specialize and perform better at specific tasks that require domain-specific knowledge."
   - content: "What are the four stages in the process of developing and implementing a plan for responsible AI when using generative models according to Microsoft's guidance?"
     choices:
     - content: "Identify potential benefits, Measure the benefits, Enhance the benefits, Operate the solution responsibly"

learn-pr/wwl-data-ai/fundamentals-generative-ai/7-exercise.yml

@@ -33,18 +33,4 @@ def add_numbers(a, b):
 
 ```
 
-## Generative AI applications  
-
-Generative AI often appears as chat-based assistants that are integrated into applications to help users find information and perform tasks efficiently. One example of such an application is [Microsoft Copilot](https://copilot.microsoft.com), an AI-powered productivity tool designed to enhance your work experience by providing real-time intelligence and assistance. All generative AI assistants utilize language models. A subset of these assistants also execute programmable tasks.  
-
-Assistants that not only produce new content, but execute tasks such as filing taxes or coordinating shipping arrangements, just as a few examples, are known as *agents*. **Agents** are applications that can respond to user input or assess situations *autonomously*, and take appropriate actions. These actions could help with a series of tasks. For example, an "executive assistant" agent could provide details about the location of a meeting on your calendar, then attach a map or automate the booking of a taxi or rideshare service to help you get there.  
-
-One way to think of different generative AI applications is by grouping them in buckets. In general, you can categorize industry and personal generative AI assistants into three buckets, each requiring more customization: ready-to-use applications, extendable applications, and applications you build from the foundation.
-
-- **Ready-to-use**: these applications are ready-to-use generative AI assistants. They do not require any programming work on the user's end to utilize the tool. You can start simply by asking the assistant a question.
-- **Extendable**: some ready-to-use applications can also be extended using your own data. These customizations enable the assistant to better support specific business processes or tasks. Microsoft Copilot is an example of technology that is ready-to-use and extendable. 
-- **Applications you build from the foundation**: you can build your own assistants and assistants with agentic capabilities starting from a language model. Many language models exist, which we will cover later on in this module. 
-
-Often, you will use services to extend or build Generative AI applications. These services provide the infrastructure, tools, and frameworks necessary to develop, train, and deploy generative AI models. For example, Microsoft provides services such as Copilot Studio to extend Microsoft 365 Copilot and Microsoft Azure AI Foundry to build AI from different models.  
-
-Next, let's build a solid understanding of how the language models in these generative AI applications work. 
+Next, let's build an understanding of the language models that power generative AI. 

learn-pr/wwl-data-ai/fundamentals-generative-ai/8-knowledge-check.yml

@@ -62,4 +62,4 @@ The *softmax* function is used within the attention function, over the scaled do
 
 The Transformer architecture uses multi-head attention, which means tokens are processed by the attention function several times in parallel. By doing so, a word or sentence can be processed multiple times, in various ways, to extract different kinds of information from the sentence.
 
-The Transformer architecture has allowed us to train models in a more efficient way. Instead of processing each token in a sentence or sequence, attention allows a model to process tokens in parallel in various ways. Next, learn how different types of language models are available for building applications.
+The Transformer architecture has allowed us to train models in a more efficient way. Instead of processing each token in a sentence or sequence, attention allows a model to process tokens in parallel in various ways. Next, learn how different types of language models are available for generative AI.