Lilly Tech Systems
Learn Azure AI Infrastructure
Master the Azure infrastructure stack for AI and ML workloads. From compute options and storage solutions to networking and enterprise security, build production-ready AI platforms on Microsoft Azure.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Overview of Azure's AI infrastructure portfolio, services, and how they fit together for enterprise AI.
2. Compute Options
Azure ML compute, GPU VMs, Azure Batch, and container-based compute for training and inference.
3. Storage
Azure Blob Storage, Data Lake, managed disks, and file shares optimized for AI data pipelines.
4. Networking
VNets, Private Endpoints, ExpressRoute, and InfiniBand networking for secure, high-performance AI.
5. Security
Azure AD, RBAC, managed identities, Key Vault, and compliance for enterprise AI deployments.
6. Best Practices
Architecture patterns, cost management, governance, and operational excellence for Azure AI infrastructure.
What You'll Learn
By the end of this course, you'll be able to:
Design AI Infrastructure
Architect Azure infrastructure for ML training, inference, and data processing at enterprise scale.
Optimize Performance
Select the right compute, storage, and networking options for maximum AI workload throughput.
Secure AI Workloads
Implement enterprise security controls including network isolation, identity management, and encryption.
Manage Costs
Optimize Azure AI spending with reserved instances, spot VMs, and right-sizing strategies.