Lilly Tech Systems
Edge AI Infrastructure
Deploy and manage AI models at the network edge. Learn to select edge devices, optimize models for constrained environments, orchestrate fleets of edge nodes, and implement reliable over-the-air update pipelines.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Understand why edge AI exists, its advantages over cloud-only inference, and the key challenges of deploying at the edge.
2. Edge Devices
Compare NVIDIA Jetson, Google Coral, Intel NCS, and other edge AI accelerators for different use cases and budgets.
3. Deployment
Package, optimize, and deploy ML models to edge devices using TensorRT, TFLite, ONNX Runtime, and container runtimes.
4. Orchestration
Manage fleets of edge devices with K3s, AWS IoT Greengrass, Azure IoT Edge, and custom orchestration platforms.
5. Updates
Implement over-the-air model updates with A/B testing, rollback capabilities, and bandwidth-efficient delta updates.
6. Best Practices
Security, monitoring, power management, and operational guidelines for production edge AI deployments.