Microsoft Azure
Learn Azure
Build secure, governed, and observable cloud solutions with practical Azure architecture patterns.
Start with the first guideYour outcome
Design cloud systems you can explain, deploy, and operate.
Core curriculum
The skills to practice
Guided path
Azure AI apps and agents developer
Prepare for AI-103 while building with Microsoft Foundry, retrieval, tools, content safety, and observability.
Suggested pace: 8 weeks · Capstone: Production RAG application
0 of 5 complete
- 01
Map the role objectives
Use the current certification outline to choose a focused project scope.
- 02
Design the Azure foundation
Document identity, network, governance, and environment boundaries.
- 03
Connect services without embedded secrets
Draw each workload identity and least-privilege resource assignment.
- 04
Build retrieval with measurable evidence
Create a small evaluation set and inspect retrieval quality before tuning.
- 05
Provision and release repeatably
Describe the deployment workflow, reviewed plan, and cleanup procedure.
Practical guides
Learn through decisions and builds.
AI-103 study roadmap with practical projects
Map the AI-103 objectives to an eight-week plan of Microsoft Foundry labs and portfolio-ready projects.
Design an Azure landing zone for enterprise AI
A decision guide for subscriptions, identity, networking, policy, data boundaries, and observability in an AI platform.
Control Azure AI costs without slowing delivery
Estimate and control Azure AI workload costs with unit economics, model and search meters, application guardrails, and Azure Cost Management alerts.
Use managed identity in Azure AI solutions
Replace supported Azure application secrets with managed identities, then map each workload-to-service permission, role scope, network rule, and diagnostic test.