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Artículo: AMZ-B0G1D3HHNL
Mojo Programming: From Prototype to Petascale: Architecting Scalable, Low-Latency AI Systems for Production
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Kindle
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0.20 kg
0.20 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- AI development today suffers from a structural imbalance. Ideas come alive quickly in Python, but production demands something entirely different: deterministic performance, tight control over memory and parallelism, and the ability to exploit every ounce of available hardware. Most teams bridge this gap with a maze of rewrites, patches, wrappers, and specialized kernels, an approach that becomes increasingly fragile and expensive as systems scale. But what if you could design, optimize, and deploy petascale AI systems without switching languages or reinventing your stack? That is the promise of Mojo. More than a high-performance variant of Python, Mojo is a cohesive systems-programming environment that spans early experimentation all the way to large-scale, latency-sensitive AI deployments. It gives you the clarity of Python, the rigor of systems languages, and the hardware reach of accelerator-specific toolchains without forcing you to juggle them all separately. Mojo: From Prototype to Petascale is a practical guide to building AI systems that can grow from a single machine to an entire data center. This book focuses not on surface-level syntax but on the deeper engineering patterns that make AI infrastructure fast, predictable, and scalable. You will learn to: Design for Scale from the Start: Shift from dynamic, exploratory code to optimized Mojo modules that leverage static types, ownership, and predictable memory behavior. Understand how to design data structures and execution paths that remain efficient as workloads grow by orders of magnitude. Harness Hardware with Precision: Work directly with GPUs, many-core CPUs, and specialized accelerators through Mojo’s unified programming model. Learn how to minimize data movement, fuse operations, break bottlenecks in memory bandwidth, and orchestrate compute across heterogeneous devices. Build Distributed and Robust AI Services: Move beyond single-node performance to architect multi-node clusters that support high-throughput inference, synchronized or sharded training, and resilient service architectures. Learn patterns for concurrency, batching, scheduling, recovery, and load regulation using Mojo’s low-level control. Open Up the AI Stack: Implement and tune your own kernels—attention mechanisms, fused MLPs, operators, and communication primitives—while retaining the readability of high-level code. Understand how Mojo translates your abstractions into highly optimized machine-level operations. Unify Development and Deployment: Replace the traditional cycle of prototyping in one ecosystem and deploying in another. With Mojo, the same codebase powers your experiments, benchmarks, production services, and device-specific kernels. This dramatically reduces iteration time and makes system behavior more transparent and reproducible. Deploy Across the Compute Spectrum: Target cloud GPUs, CPU clusters, edge devices, and embedded accelerators from the same source code. Optimize once, then retune and recompile for each environment instead of re-engineering your entire pipeline. This book is written for engineers responsible for large-scale AI systems, researchers pushing hardware limits, and architects designing the next generation of AI platforms. If you’ve ever struggled with the friction between rapid experimentation and production reliability, this guide will give you the tools and patterns to remove that divide completely. By the final page, you will have a practical playbook for building AI systems that exist comfortably at any scale, from a single prototype to petascale deployments powered entirely by the Mojo programming language.
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