Lilly Tech Systems
Edge AI Architecture
Design AI systems for edge deployment with model compression, quantization, and hybrid cloud-edge patterns.
Course Lessons
Follow these lessons in order for a complete understanding of edge ai architecture.
1. Edge AI Overview
Learn about edge ai overview in the context of edge ai architecture.
2. Model Compression Techniques
Learn about model compression techniques in the context of edge ai architecture.
3. Quantization and Pruning
Learn about quantization and pruning in the context of edge ai architecture.
4. TensorFlow Lite Architecture
Learn about tensorflow lite architecture in the context of edge ai architecture.
5. ONNX Runtime on Edge
Learn about onnx runtime on edge in the context of edge ai architecture.
6. Edge-Cloud Hybrid Architecture
Learn about edge-cloud hybrid architecture in the context of edge ai architecture.
7. Edge Deployment Patterns
Learn about edge deployment patterns in the context of edge ai architecture.
What You'll Learn
By the end of this course, you will be able to:
Understand Core Concepts
Gain deep understanding of the principles and patterns that define edge ai architecture.
Apply in Practice
Implement real-world solutions using the architectural patterns and code examples from each lesson.
Make Architecture Decisions
Evaluate trade-offs and choose the right approaches for your specific requirements and constraints.
Build Production Systems
Design and implement production-ready AI systems that are reliable, scalable, and maintainable.