"Ultimate ONNX for Deep Learning Optimization" is a comprehensive guide aimed at ML Engineers and Embedded Developers, focusing on deploying machine learning models to resource-constrained edge devices. The book addresses the challenges of moving models from research to production, offering a detailed workflow from model export to deployment. It covers ONNX fundamentals, optimization techniques such as quantization and pruning, and practical tools like ONNX Runtime. Real-world case studies are included, demonstrating the deployment of models like YOLOv12 and Whisper on devices like the Raspberry Pi. This guide is essential for those looking to optimize deep learning models for speed and efficiency without compromising accuracy. This matters because effectively deploying machine learning models on edge devices can significantly enhance the performance and applicability of AI in real-world scenarios.
