Lilly Tech Systems
Hybrid Cloud AI Architecture
Design AI systems that seamlessly span on-premises data centers and public cloud. Learn design patterns for hybrid training and inference, data synchronization, secure networking, and unified security across environments.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Understand why organizations choose hybrid cloud for AI and the key architectural decisions involved.
2. Design Patterns
Learn proven patterns for splitting AI workloads between on-premises and cloud: burst training, split inference, and federated learning.
3. Data Synchronization
Synchronize training data and model artifacts between on-premises and cloud with consistency, efficiency, and compliance.
4. Networking
Connect on-premises GPU clusters to cloud services with Direct Connect, ExpressRoute, VPN, and service mesh architectures.
5. Security
Implement unified identity, encryption, compliance controls, and zero-trust security across hybrid AI environments.
6. Best Practices
Cost optimization, operational excellence, migration planning, and governance for hybrid cloud AI deployments.