Fix duplicate title issues and improve formatting

staleycyn · staleycyn · commit f52648765db0 · 2026-01-16T08:13:27.000-08:00
- Made all title fields consistent with metadata.title across all unit YAML files
- Fixed tip formatting in manage.md using proper Microsoft Learn markdown syntax
- This resolves the duplicate title display issue in published content
diff --git a/learn-pr/wwl-azure/design-ai-workloads/1-introduction.yml b/learn-pr/wwl-azure/design-ai-workloads/1-introduction.yml
@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.introduction
-title: "Introduction"
+title: "Introduction to AI Workloads Design"
 metadata:
   title: "Introduction to AI Workloads Design"
   description: "Learn to design AI workloads on Azure, align strategy with goals, and apply best practices for governance, security, and responsible AI."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/10-summary.yml b/learn-pr/wwl-azure/design-ai-workloads/10-summary.yml
@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.summary
-title: "Summary"
+title: "Summary of AI Workload Design"
 metadata:
   title: "Summary of AI Workload Design"
   description: "Learn how to design Azure AI workloads using SaaS, PaaS, and IaaS services while aligning solutions with business goals and best practices."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/2-strategy.yml b/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 for Azure workloads"
+title: "Create an AI Strategy in Azure"
 metadata:
   title: "Create an AI Strategy in Azure"
   description: "Learn how to create an AI strategy using Azure SaaS, PaaS, and IaaS solutions to meet your organization's needs effectively."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/3-adoption-plan.yml b/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 effectively"
+title: "Plan AI Adoption Effectively"
 metadata:
   title: "Plan AI Adoption Effectively"
   description: "Learn how to create an AI adoption plan to align projects with business priorities, address skill gaps, and deliver results efficiently."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/4-platform-workloads.yml b/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 on Azure"
+title: "Design PaaS AI Workloads on Azure"
 metadata:
   title: "Design PaaS AI Workloads on Azure"
   description: "Learn how to design scalable and secure PaaS AI workloads on Azure, including generative AI, nongenerative AI, and secure networking."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/5-infrastructure-workloads.yml b/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 with Azure"
+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."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/6-governance.yml b/learn-pr/wwl-azure/design-ai-workloads/6-governance.yml
@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.governance
-title: "Govern AI workloads effectively"
+title: "Govern AI Workloads"
 metadata:
   title: "Govern AI Workloads"
   description: "Learn how to govern AI workloads responsibly by applying principles, managing risks, and ensuring security, compliance, and cost control."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/7-secure-workloads.yml b/learn-pr/wwl-azure/design-ai-workloads/7-secure-workloads.yml
@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.secure-workloads
-title: "Secure AI workloads in Azure"
+title: "Secure AI Workloads in Azure"
 metadata:
   title: "Secure AI Workloads in Azure"
   description: "Learn to secure AI workloads by identifying risks, protecting resources, and safeguarding data."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/8-manage.yml b/learn-pr/wwl-azure/design-ai-workloads/8-manage.yml
@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.manage
-title: "Manage AI workloads effectively"
+title: "Manage AI Workloads"
 metadata:
   title: "Manage AI Workloads"
   description: "Learn how to manage AI workloads effectively, including operations, deployment, costs, and business continuity, using Azure tools and best practices."
diff --git a/learn-pr/wwl-azure/design-ai-workloads/9-knowledge-check.yml b/learn-pr/wwl-azure/design-ai-workloads/9-knowledge-check.yml
@@ -1,6 +1,6 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.design-ai-workloads.knowledge-check
-title: "Module assessment"
+title: "Knowledge check"
 metadata:
   title: "Knowledge check"
   description: "Knowledge check"
diff --git a/learn-pr/wwl-azure/design-ai-workloads/includes/2-strategy.md b/learn-pr/wwl-azure/design-ai-workloads/includes/2-strategy.md
@@ -32,7 +32,7 @@ PaaS solutions are suited for teams that need more control over their AI workflo
 
 - [Azure Container Apps](/azure/container-apps/overview) is a serverless platform that allows you to maintain less infrastructure and save costs while running containerized applications.
 
-- [Azure AI service](/ai-services/what-are-ai-services) helps create applications with customizable APIs and models. Example applications include natural language, speech, and vision processing. 
+- Azure AI service helps create applications with customizable APIs and models. Example applications include natural language, speech, and vision processing. 
 
 ### Build AI models with Azure infrastructure services (IaaS) for maximum control
 
diff --git a/learn-pr/wwl-azure/design-ai-workloads/includes/3-adoption-plan.md b/learn-pr/wwl-azure/design-ai-workloads/includes/3-adoption-plan.md
@@ -10,10 +10,10 @@ Use a skills-and-data readiness framework to objectively evaluate your organizat
 
 | Level | Skills| Data Readiness | Feasible use cases |
 | - | - | - | - |
-| Level 1 | Basic AI concepts, simple prompts| Minimal to enterprise data | Azure quick start projects, Copilot solutions |
-| Level 2 | Model selection, deployment basics, data cleaning| Minimal too small, structured datasets | Analytical AI, generative AI app (no RAG), and simple ML app| 
-| Level 3 | Prompt engineering, preprocessing, chunking| Historical business data available | Generative AI app with RAG, custom ML model, small AI model on Azure VMs |
-| Level 4 | Advanced ML, orchestration, optimization| Large high-quality datasets | Large AI workloads on VMs, AKS, or ACA |
+| 1 | Basic AI concepts, simple prompts| Minimal to enterprise data | Azure quick start projects, Copilot solutions |
+| 2 | Model selection, deployment basics, data cleaning| Minimal too small, structured datasets | Analytical AI, generative AI app (no RAG), and simple ML app| 
+| 3 | Prompt engineering, preprocessing, chunking| Historical business data available | Generative AI app with RAG, custom ML model, small AI model on Azure VMs |
+| 4 | Advanced ML, orchestration, optimization| Large high-quality datasets | Large AI workloads on VMs, AKS, or ACA |
 
 ### Acquire AI skills 
 
diff --git a/learn-pr/wwl-azure/design-ai-workloads/includes/5-infrastructure-workloads.md b/learn-pr/wwl-azure/design-ai-workloads/includes/5-infrastructure-workloads.md
@@ -11,7 +11,7 @@ You don’t need a separate 'AI landing zone' to get started. AI services can be
 :::image type="content" source="../media/virtual-machine-concepts.png" alt-text="Diagram of a virtual machine with platform concepts.":::
 
 
-[AI workloads](/azure/cloud-adoption-framework/scenarios/ai/infrastructure/compute)) rely on specialized virtual machines (VMs) to handle intensive computation, large datasets, and accelerated training or inferencing. When you use curated VM images, such as Data Science Virtual Machines, your team gets started quickly with preinstalled AI tools and frameworks.
+[AI workloads](/azure/cloud-adoption-framework/scenarios/ai/infrastructure/compute) rely on specialized virtual machines (VMs) to handle intensive computation, large datasets, and accelerated training or inferencing. When you use curated VM images, such as Data Science Virtual Machines, your team gets started quickly with preinstalled AI tools and frameworks.
 
 Choosing the right VM size is critical to balancing performance and cost, especially when distinguishing between training and inference workloads. As AI solutions scale, compute orchestration improves efficiency and reproducibility across VM clusters, while containers provide a consistent, portable way to package and run AI workloads across environments.
 
diff --git a/learn-pr/wwl-azure/design-ai-workloads/includes/8-manage.md b/learn-pr/wwl-azure/design-ai-workloads/includes/8-manage.md
@@ -2,7 +2,8 @@
 
 Managing AI operations is about keeping your AI work consistent and reliable. Create an [AI Center of Excellence](/azure/cloud-adoption-framework/scenarios/ai/center-of-excellence) to guide strategy, choose the right framework (MLOps or GenAIOps), and standardize SDKs and APIs. Use sandboxes for safe experimentation and simplify tuning with tools like [Copilot Tuning.](/copilot/microsoft-365/copilot-tuning-overview) 
 
-Tip: For more information, consider the [Introduction to AI Center of Excellence](/training/modules/intro-ai-center-excellence/) training module.
+> [!TIP]
+> For more information, consider the [Introduction to AI Center of Excellence](/training/modules/intro-ai-center-excellence/) training module.
 
 :::image type="content" source="../media/manage-operations.png" alt-text="Diagram of the main management areas.":::
 
