Machine Learning and Rust: From Fundamentals to Production

The book "Machine Learning and Rust" is a practical guide for developers who want to master building high-performance machine learning systems in the Rust language. Unlike traditional Python libraries, Rust offers safe memory management, zero overhead, and ideal performance for production systems.

What this book is about

The author systematically guides the reader from the basics of machine learning to deploying ready-made models. You will learn how to:

• Implement linear regression, logistic regression, and decision trees in pure Rust
• Work with popular crates: ndarray for numerical computations, smartcore for classical algorithms, tch-rs for neural networks based on PyTorch
• Create and train neural networks from scratch, including convolutional and recurrent architectures
• Optimize performance: parallel computing with Rayon, SIMD instructions, working with GPU via Vulkan and CUDA
• Integrate models into web services using Actix-web and Rocket
• Deploy ML systems in Docker containers and on edge devices

Who this book is for

• Rust developers looking to enter Data Science
• ML engineers tired of slow Python and seeking an alternative for production
• Systems programmers who need to add machine learning to their projects
• Students and researchers interested in high-performance computing

What you will learn

After reading, you will be able to confidently:

• Design and implement ML pipelines in Rust
• Work with real datasets: loading, cleaning, normalization
• Create custom layers and loss functions for neural networks
• Profile and optimize code for maximum performance
• Integrate trained models into a production environment

Why Rust for ML?

Rust is not just "another language." It is a tool that changes the rules of the game in machine learning. Memory safety at compile time eliminates entire classes of errors, and zero-cost abstractions allow you to write code that runs at the speed of C. The book will show how to apply these advantages in practice.

Each chapter contains practical examples, ready-to-run projects, and exercises for independent work. The source code for all examples is available on GitHub.