Lilly Tech Systems
Designing AI for Edge Devices
Deploy production AI models on edge hardware — from NVIDIA Jetson and Google Coral to Raspberry Pi and mobile phones. Learn model optimization (quantization, pruning, distillation), inference runtimes (TFLite, CoreML, TensorRT), edge-cloud synchronization, offline operation, and fleet management at scale.
Your Learning Path
Follow these lessons in order to design a complete edge AI deployment pipeline, or jump to any topic you need right now.
1. Edge AI Architecture
Why edge AI matters (latency, privacy, bandwidth, cost), edge vs cloud vs hybrid decision framework, edge hardware landscape (Jetson, Coral, RPi, mobile), and real-world use cases across industries.
2. Model Optimization for Edge
Quantization (PTQ, QAT), structured and unstructured pruning, knowledge distillation, edge-optimized architectures (MobileNet, EfficientNet), ONNX conversion, and size/accuracy trade-off benchmarks.
3. Edge Inference Runtimes
TFLite, CoreML, ONNX Runtime Mobile, TensorRT for Jetson, OpenVINO, NNAPI. Runtime comparison benchmarks and production deployment code for each platform.
4. Edge-Cloud Synchronization
OTA model update distribution, federated learning basics, data collection from edge devices, sync strategies (periodic, event-driven), and bandwidth management.
5. Offline & Intermittent Connectivity
Offline-first architecture, local model fallback, data queuing and sync, conflict resolution, and graceful degradation patterns for unreliable networks.
6. Fleet Management at Scale
Managing thousands of edge devices, remote monitoring and health checks, A/B testing on edge, rollback mechanisms, and OTA update orchestration.
7. Best Practices & Checklist
Edge AI deployment checklist, power consumption optimization, thermal management, hardware selection guide, and comprehensive FAQ for edge AI engineers.
What You'll Learn
By the end of this course, you will be able to:
Optimize Models for Edge
Reduce model size by 4-10x using quantization, pruning, and distillation while maintaining accuracy within 1-2% of the original model.
Deploy on Any Hardware
Ship models to Jetson, Coral, Raspberry Pi, iOS, and Android using the right inference runtime for each platform with production-ready code.
Build Offline-First Systems
Design architectures that work without connectivity, queue data intelligently, and sync when networks are available without data loss.
Manage Device Fleets
Orchestrate OTA updates, monitor device health, run A/B tests, and roll back failed deployments across thousands of edge devices.