Merge pull request #49394 from madiepev/genaiops-1

small updates

Author: Stacyrch140

learn-pr/paths/operationalize-gen-ai-apps/index.yml

@@ -28,7 +28,6 @@ subjects:
 - natural-language-processing
 modules:
 - learn.wwl.plan-prepare-genaiops
-- learn.wwl.ai-foundry-sdk
 - learn.evaluate-generative-ai-apps
 trophy:
   uid: learn.wwl.operationalize-gen-ai-apps.trophy

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/1-introduction.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Introduction
   description: "Introduction to configuring the deployment of a language model using the Azure AI Foundry portal."
-  ms.date: 11/25/2024
+  ms.date: 03/05/2025
   author: madiepev
   ms.author: madiepev
   ms.topic: unit

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/2-select-model.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Explore the language models in the model catalog
   description: "Learn how to navigate the model catalog and to select a language model."
-  ms.date: 11/25/2024
+  ms.date: 03/05/2025
   author: madiepev
   ms.author: madiepev
   ms.topic: unit

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/3-deploy-model.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Deploy a model to an endpoint
   description: "Deploy and test a language model in the Azure AI Foundry portal."
-  ms.date: 11/25/2024
+  ms.date: 03/05/2025
   author: madiepev
   ms.author: madiepev
   ms.topic: unit

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/4-improve-model.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Improve the performance of a language model
   description: "Explore the various strategies to improve the model's performance"
-  ms.date: 11/25/2024
+  ms.date: 03/05/2025
   author: madiepev
   ms.author: madiepev
   ms.topic: unit

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/5-exercise.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Exercise - Explore, deploy, and chat with language models
   description: "Exercise - Explore, deploy, and chat with language models in the Azure AI Foundry portal."
-  ms.date: 11/25/2024
+  ms.date: 03/05/2025
   author: madiepev
   ms.author: madiepev
   ms.topic: unit

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/6-knowledge-check.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Knowledge check
   description: "Knowledge check about configuring language models."
-  ms.date: 11/25/2024
+  ms.date: 03/05/2025
   author: madiepev
   ms.author: madiepev
   ms.topic: unit

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/7-summary.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Summary
   description: "Summary of configuring language models."
-  ms.date: 11/25/2024
+  ms.date: 03/05/2025
   author: madiepev
   ms.author: madiepev
   ms.topic: unit

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/includes/1-introduction.md

@@ -10,6 +10,7 @@ Natural language processing (NLP) is a type of artificial intelligence (AI) that
 - **Entity extraction**. For example, extract keywords or names from a document.
 - **Text summarization**. For example, generate a short one-paragraph summary from a multi-page document.
 - **Question answering**. For example, provide answers to questions like "What is the capital of France?"
+- **Reasoning**. For example, solve a mathematical problem.
 
 > [!Note]
 > In this module, you focus on exploring foundation models used for question answering. The foundation models you explore can be used for chat applications in which you use a language model to generate a response to a user's question.

learn-pr/wwl-data-ai/explore-models-azure-ai-studio/includes/3-deploy-model.md

@@ -24,7 +24,14 @@ Now that you understand why you want to deploy a model, let's explore the deploy
 
 When you deploy a language model with Azure AI Foundry, you have several types available, which depend on the model you want to deploy:
 
-|Activity|Azure OpenAI models|Models deployed as Serverless APIs (pay-as-you-go)|Models deployed with user-managed compute|
-|---|---|---|---|
-|Deploy the model|No, you aren't billed for deploying an Azure OpenAI model to your project.|Yes, you're billed minimally per the infrastructure of the endpoint.|Yes, you're billed for the infrastructure hosting the model per minute.|
-|Call the endpoint|Yes, you're billed based on your token usage.|Yes, you're billed based on your token usage.|None.|
+- Azure OpenAI service to deploy [Azure OpenAI models](/azure/ai-services/openai/concepts/models?azure-portal=true).
+- Azure AI model inference to deploy [Azure OpenAI models and Models as a Service](/azure/ai-foundry/model-inference/concepts/models?azure-portal=true).
+- Serverless APIs to deploy [Models as a Service](/azure/ai-foundry/how-to/model-catalog-overview#content-safety-for-models-deployed-via-serverless-apis?azure-portal=true).
+- Managed compute to deploy [open-source and custom models](/azure/ai-foundry/how-to/model-catalog-overview#availability-of-models-for-deployment-as-managed-compute?azure-portal=true).
+
+The associated cost will depend on the type of model you deploy, which deployment option you choose, and what you are doing with the model:
+
+|Activity|Azure OpenAI models|Azure AI model inference|Models deployed as Serverless APIs (pay-as-you-go)|Models deployed with user-managed compute|
+|---|---|---|---|---|
+|Deploy the model|No, you aren't billed for deploying an Azure OpenAI model to your project.|No, you aren't billed for deploying an Azure OpenAI model to your project.|Yes, you're billed minimally per the infrastructure of the endpoint.|Yes, you're billed for the infrastructure hosting the model per minute.|
+|Call the endpoint|Yes, you're billed based on your token usage.|Yes, you're billed based on your token usage.|Yes, you're billed based on your token usage.|None.|