Rust for AI Systems: Building High-Performance Machine Learning Infrastructure with Rust
Artificial intelligence has moved beyond experimentation. Today’s models must be trained, deployed, and scaled across massive datasets and production environments. This shift has exposed the limits of traditional tooling: performance bottlenecks, concurrency issues, and fragile systems built on layers of extensions.
Rust for AI Systems shows how to bring the power of Rust into machine learning infrastructure. Rust’s unique combination of performance, memory safety, and concurrency makes it one of the strongest languages for building reliable, high-throughput AI systems. This book provides a practical path for engineers who want to strengthen the foundations of their AI pipelines with Rust.
Inside, you will learn how to:
Use Rust to build fast and memory-safe data processing pipelines for AI workloads.
Integrate Rust with Python and existing ML frameworks for seamless interoperability.
Design and implement high-performance inference services with Rust’s concurrency model.
Apply Rust in natural language processing, data engineering, and model-serving contexts.
Understand the trade-offs and performance benefits Rust provides compared to other languages.
Written for machine learning engineers, systems programmers, and Rust developers, this book balances clear explanations with detailed, idiomatic code examples. Each concept is tied directly to practical AI use cases, from preprocessing and optimization to deployment at scale.
Rust for AI Systems is not about replacing the existing AI ecosystem. It is about strengthening it. With Rust, you can build infrastructure that is fast, safe, and designed to scale with the demands of modern machine learning.
Whether you are an AI engineer looking to move beyond Python bottlenecks or a Rust developer ready to step into machine learning, this book provides the knowledge and practical workflows to apply Rust where it matters most: at the foundation of AI systems.
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Paperback. Condición: new. Paperback. Rust for AI Systems: Building High-Performance Machine Learning Infrastructure with RustArtificial intelligence has moved beyond experimentation. Today's models must be trained, deployed, and scaled across massive datasets and production environments. This shift has exposed the limits of traditional tooling: performance bottlenecks, concurrency issues, and fragile systems built on layers of extensions.Rust for AI Systems shows how to bring the power of Rust into machine learning infrastructure. Rust's unique combination of performance, memory safety, and concurrency makes it one of the strongest languages for building reliable, high-throughput AI systems. This book provides a practical path for engineers who want to strengthen the foundations of their AI pipelines with Rust.Inside, you will learn how to: Use Rust to build fast and memory-safe data processing pipelines for AI workloads.Integrate Rust with Python and existing ML frameworks for seamless interoperability.Design and implement high-performance inference services with Rust's concurrency model.Apply Rust in natural language processing, data engineering, and model-serving contexts.Understand the trade-offs and performance benefits Rust provides compared to other languages.Written for machine learning engineers, systems programmers, and Rust developers, this book balances clear explanations with detailed, idiomatic code examples. Each concept is tied directly to practical AI use cases, from preprocessing and optimization to deployment at scale.Rust for AI Systems is not about replacing the existing AI ecosystem. It is about strengthening it. With Rust, you can build infrastructure that is fast, safe, and designed to scale with the demands of modern machine learning.Whether you are an AI engineer looking to move beyond Python bottlenecks or a Rust developer ready to step into machine learning, this book provides the knowledge and practical workflows to apply Rust where it matters most: at the foundation of AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798298790109
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