Building Machine Learning APIs: From PyTorch Training to High-Performance Inference with ONNX and FastAPI
This book guides you through creating machine learning APIs, starting from PyTorch model training and moving to high-performance inference with ONNX. Using FastAPI, you'll learn to deploy scalable, efficient services for real-world ML applications, bridging development and production seamlessly.
About This Book
Building Machine Learning APIs provides a comprehensive approach to deploying machine learning models effectively. It starts with training models using PyTorch and progresses to optimizing them for inference with ONNX.
The book emphasizes high-performance techniques to ensure models run efficiently in real-world applications. Readers learn to integrate these optimized models into APIs using FastAPI, a modern web framework.
Practical examples illustrate the entire pipeline, from model development to serving predictions via scalable endpoints. This resource is ideal for developers aiming to productionize ML workflows.
By focusing on key tools like PyTorch, ONNX, and FastAPI, the guide equips practitioners with the skills to build reliable and performant ML services.
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