SKU/Artículo: AMZ-B0FJMPCJYN

Machine Learning with Rust: A Hands-on Guide to Building, Training, and Deploying Intelligent Models

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Paperback

Hardcover

Kindle

Paperback

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En stock
Peso con empaque:
0.58 kg
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Condición
Nuevo
Producto de:
Amazon
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USA

Sobre este producto
  • Machine Learning with Rust is your hands-on guide to building, training, and deploying high-performance ML models from scratch. This book takes you on a complete journey, starting with high-speed data engineering using Polars, moving through classical and deep learning with Linfa and tch-rs, and culminating in the deployment of a live, production-ready API with Axum and Docker.You've built models in Python. They work in a notebook, but you know the truth: when it's time for production, the performance gaps appear. The latency spikes. The memory usage balloons. You're left wondering if your brilliant model can ever survive in the real world. This book tells the story of that escape. It's about breaking free from the limits of interpreted languages and embracing a tool built for the demands of production. It's the journey from a slow prototype to an intelligent, high-performance system that just works. What's Inside:This isn't just a book about theory; it's a book about building. You will learn to:Master Polars to build data pipelines that are orders of magnitude faster than their Python equivalents.Train and evaluate a full range of models, from classical regressions to advanced Gradient Boosting.Unlock the power of deep learning with Rust's PyTorch bindings, including practical transfer learning with pre-trained models like ResNet.Deploy your models as robust, asynchronous web APIs using the modern Axum framework.Package your entire application into a small, secure, and optimized Docker container, ready for any cloud environment. Who It's Meant For:This book is for you if you are:A Python developer or data scientist who wants to build production-ready ML systems with elite performance.A Rust developer curious about breaking into the exciting field of machine learning.An engineer tasked with deploying ML models where speed, memory efficiency, and reliability are non-negotiable.The world of AI is moving beyond the notebook. The future belongs to those who can build systems that are not just smart, but also fast, safe, and scalable. Are you ready to stop shipping prototypes and start deploying professional, high-performance intelligent systems?Your journey starts now. Pick up this book and build the future of machine learning.
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