Update media files and add lightbox functionality

- Updated media files with latest versions: landing-zones.png, storage-concepts-1.png, traditional-workflow.png, generative-workflow.png, planning-steps.png
- Added lightbox functionality to generative and traditional workflow images in platform workloads unit
- Updated YAML and markdown files with content improvements
- Enhanced user experience with clickable diagrams for detailed viewing

Author: staleycyn

learn-pr/wwl-azure/design-ai-workloads/2-strategy.yml

@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.strategy
-title: "Create an AI Strategy"
+title: "Create an AI strategy"
 metadata:
   title: "Create an AI Strategy"
   description: "Learn how to create an AI strategy using Azure SaaS, PaaS, and IaaS solutions to meet your organization's needs effectively."

learn-pr/wwl-azure/design-ai-workloads/3-adoption-plan.yml

@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.adoption-plan
-title: "Plan AI Adoption"
+title: "Plan AI adoption"
 metadata:
   title: "Plan AI Adoption"
   description: "Learn how to create an AI adoption plan to align projects with business priorities, address skill gaps, and deliver results efficiently."

learn-pr/wwl-azure/design-ai-workloads/4-platform-workloads.yml

@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.platform-workloads
-title: "Design PaaS AI Workloads"
+title: "Design PaaS AI workloads"
 metadata:
   title: "Design PaaS AI Workloads"
   description: "Learn how to design scalable and secure PaaS AI workloads on Azure, including generative AI, nongenerative AI, and secure networking."

learn-pr/wwl-azure/design-ai-workloads/5-infrastructure-workloads.yml

@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.infrastructure-workloads
-title: "Design IaaS AI Workloads"
+title: "Design IaaS AI workloads"
 metadata:
   title: "Design IaaS AI Workloads"
   description: "Learn how to design IaaS AI workloads using Azure landing zones for scalable, efficient, and secure AI deployment and management."

learn-pr/wwl-azure/design-ai-workloads/includes/1-introduction.md

@@ -2,7 +2,7 @@ This module helps you design and implement AI workloads on Azure. You learn how
 
 :::image type="content" source="../media/strategy-steps.png" alt-text="Diagram of the units in the module.":::
 
-### Learning Objectives
+### Learning objectives
 
 - Select SaaS, PaaS, or IaaS AI solutions based on business needs and required levels of control.
 

learn-pr/wwl-azure/design-ai-workloads/includes/4-platform-workloads.md

@@ -6,7 +6,7 @@ When you're designing AI workloads on Azure PaaS, focus on three things: choose
 
 In this example, Microsoft Foundry orchestrates the answer to a user’s search query. AI Search uses grounding data to provide an answer. 
 
-:::image type="content" source="../media/generative-workflow.png" alt-text="Diagram of a generative workflow.":::
+:::image type="content" source="../media/generative-workflow.png" alt-text="Diagram of a generative workflow." lightbox="../media/generative-workflow.png":::
 
 To build effective applications, select resources that ground AI models and deliver accurate results. Ensure you match the right tools to the type of AI you’re building. For generative AI you’re basically taking a user question, finding the right data to ground the answer, and passing everything through a model. That means you pick a generative AI platform (Foundry or Azure OpenAI), an orchestrator to handle the workflow, something like Azure AI Search to retrieve relevant data, and the place where your data lives, such as Blob Storage.
 
@@ -19,7 +19,7 @@ To build effective applications, select resources that ground AI models and deli
 
 In this example, an AI app uses Azure AI Services and Azure Machine Learning to provide a response. 
 
-:::image type="content" source="../media/traditional-workflow.png" alt-text="Diagram of a nongenerative workflow.":::
+:::image type="content" source="../media/traditional-workflow.png" alt-text="Diagram of a nongenerative workflow." lightbox="../media/traditional-workflow.png":::
 
 Select resources that help you build AI workloads with prebuilt or custom solutions. You send data into a model and get results—no grounding step required. You can use prebuilt AI services if you want something simple with no extra compute, or Azure Machine Learning if you need to train or customize a model. Either way, you choose the compute you need, your data source, and any optional processing tools.
 

learn-pr/wwl-azure/design-ai-workloads/includes/7-secure-workloads.md

@@ -30,7 +30,7 @@ Next, build incident response plans tailored to AI. Plans should outline how you
 
 Finally, monitor each AI platform. Different architectures have different risks, so platform-specific monitoring ensures you’re covering all potential attack paths.
 
-### Tool Summary
+### Tool summary
 
 This table provides a summary of some of the security tools and processes.