ONNX for AI Developers: The Complete Guide to Model Conversion, Optimization, and Deployment
Unlock true AI portability and production power with ONNX. Move your machine learning models seamlessly between frameworks-and deploy them anywhere, faster than ever.
Are you tired of being locked into a single ML framework, or struggling to deploy models outside your development environment? This book is your one-stop, practical guide to ONNX-the open standard transforming how AI gets built, optimized, and deployed in real-world applications. Whether you're working with PyTorch, TensorFlow, scikit-learn, or edge devices, you'll learn how to convert, streamline, and productionize your models with confidence.
Inside, you'll discover hands-on tutorials for exporting from the most popular frameworks, optimizing with ONNX Runtime, and deploying models on cloud, edge, and mobile platforms. Each chapter demystifies core ONNX concepts, best practices, and real-world pitfalls-so you can deliver scalable, maintainable AI solutions at every stage of the ML lifecycle.
Why Choose This Book?
Framework Agnostic: Step-by-step guidance for converting models from PyTorch, TensorFlow, Keras, and scikit-learn.
Production-Focused: Proven techniques for optimizing performance and serving models with FastAPI, Docker, and Kubernetes.
Real-World Projects: Complete end-to-end examples for vision, NLP, and custom pipelines.
Troubleshooting & Best Practices: Practical advice for debugging, compatibility, and future-proofing your ML stack.
Beginner to Pro: Clear explanations for newcomers, with expert insights for advanced developers.
Ready to build, optimize, and deploy AI anywhere?
Get your copy of ONNX for AI Developers and future-proof your machine learning workflow today
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