Merge pull request #54069 from staleycyn/patch-3

Content drift on the Design AI Workloads module

Author: JamesJBarnett

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

@@ -8,7 +8,7 @@ SaaS offerings are turnkey AI solutions like Microsoft 365 Copilot, Copilot Stud
 
 SaaS offerings require minimal setup and are ideal for organizations seeking rapid productivity gains and role-specific AI features.
 
-- [Microsoft Copilot](/azure/copilot/overview) is a free web-grounded chat application. Copilot Pro provides better performance, capacity, and access to Copilot in certain Microsoft 365 apps.
+- Microsoft Copilot is a free web-grounded chat application. Copilot Pro provides better performance, capacity, and access to Copilot in certain Microsoft 365 apps.
 
 - [Microsoft 365 Copilot](/copilot/microsoft-365/microsoft-365-copilot-overview) provides web-grounded chat and in-app AI assistance across Microsoft 365 applications, integrating with your Microsoft Graph data.
 
@@ -22,9 +22,9 @@ Platform services include Microsoft Foundry, Azure OpenAI, Azure Machine Learnin
 
 PaaS solutions are suited for teams that need more control over their AI workflows and want to apply prebuilt APIs or custom models.
 
-- [Microsoft Foundry](/azure/ai-foundry/what-is-azure-ai-foundry) is a unified Azure platform-as-a-service offering for enterprise AI operations, model builders, and application development. This foundation combines production-grade infrastructure with friendly interfaces, enabling developers to focus on building applications rather than managing infrastructure.
+- [Microsoft Foundry](/azure/ai-foundry/what-is-foundry) is a unified Azure platform-as-a-service offering for enterprise AI operations, model builders, and application development. This foundation combines production-grade infrastructure with friendly interfaces, enabling developers to focus on building applications rather than managing infrastructure.
 
-- [Azure Machine Learning](/azure/machine-learning/overview-what-is-azure-machine-learning) is a unified Azure platform-as-a-service offering for enterprise AI operations, model builders, and application development. This foundation combines production-grade infrastructure with friendly interfaces, enabling developers to focus on building applications rather than managing infrastructure.
+- [Azure Machine Learning](/azure/machine-learning/overview-what-is-azure-machine-learning) is a cloud service that accelerates and manages the machine learning project lifecycle. ML professionals, data scientists, and engineers use it in their daily workflows to train and deploy models and manage machine learning operations. Use Azure ML when you need open-source framework support for custom model development.
 
 - [Azure OpenAI](/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure) provides access to generative AI models including a family of large and small neural network language models. Models operate within a scalable and securable cloud service on Azure.
 
@@ -41,4 +41,4 @@ Azure infrastructure services give you the most control when your AI workload ha
 For example, a healthcare organization might deploy its workloads on Azure virtual machines. Using Azure IaaS gives the team complete control over the software, hardware, and data on those machines. While IaaS offers maximum flexibility and control, it also comes with higher operational effort and requires deeper AI and infrastructure expertise.
 
 > [!TIP]
-> Take a moment to review the [AI product decision tree](/azure/cloud-adoption-framework/scenarios/ai/strategy#microsoft-ai-decision-tree). Which AI products best align with your organization’s goals?
+> Take a moment to review the [AI product decision tree](/azure/cloud-adoption-framework/scenarios/ai/strategy#microsoft-ai-decision-tree). Which AI products best align with your organization’s goals? If your strategy includes agentic workloads, review the [AI agent decision tree](/azure/cloud-adoption-framework/ai-agents/technology-solutions-plan-strategy#ai-agent-decision-tree) for guidance on choosing between SaaS agents, Copilot Studio, and Microsoft Foundry.

learn-pr/wwl-azure/design-ai-workloads/includes/6-governance.md

@@ -2,7 +2,7 @@ Governing AI is about making sure your organization uses AI safely, responsibly,
 
 ### Apply AI principles
 
-Begin by assessing risks across your organization. Microsoft recommends evaluating risks through [Responsible AI principles](https://www.microsoft.com/ai/principles-and-approach). These principles include accountability, fairness, reliability, privacy, inclusiveness, and transparency.
+Begin by assessing risks across your organization. Microsoft recommends evaluating risks through [Responsible AI principles](https://www.microsoft.com/ai/principles-and-approach). These principles include accountability, fairness, reliability and safety, privacy and security, inclusiveness, and transparency.
 
 :::image type="content" source="../media/principles.png" alt-text="Diagram of the basic artificial intelligence principles.":::
 

learn-pr/wwl-azure/design-ai-workloads/includes/8-manage.md

@@ -1,4 +1,4 @@
-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) 
+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) (early access preview).  
 
 > [!TIP]
 > To learn more, consider the [Introduction to AI Center of Excellence](/training/modules/intro-ai-center-excellence/) training module.
@@ -8,7 +8,7 @@ Managing AI operations is about keeping your AI work consistent and reliable. Cr
 
 ### AI deployment 
 
-Use the deployment model that fits how your org works, usually one Foundry instance per business unit, with separate projects for each use case. Deploy in the correct Azure regions for performance and compliance. Monitor resources with Azure Monitor and manage configurations and costs through the [Foundry Management Center](/azure/ai-foundry/control-plane/how-to-manage-compliance-security). Use API Management when multiple apps are deployed.
+Use the deployment model that fits how your org works, usually one Foundry instance per business unit, with separate projects for each use case. Deploy in the correct Azure regions for performance and compliance. Monitor resources with Azure Monitor and manage configurations and costs through the [Foundry Control Plane](/azure/ai-foundry/control-plane/how-to-manage-compliance-security). Use API Management when multiple apps are deployed.
 
 ### AI models 
 

learn-pr/wwl-azure/design-ai-workloads/index.yml

@@ -3,7 +3,7 @@ uid: learn.wwl.design-ai-workloads
 metadata:
   title: "Design AI Workloads on Azure"
   description: "Learn to design AI workloads on Azure using SaaS, PaaS, and IaaS solutions. Align AI strategy with business goals and apply governance best practices."
-  ms.date: 01/26/2026
+  ms.date: 03/23/2026
   author: wwlpublish
   ms.author: cynthist
   ms.topic: module