Chris Fregly

AI Systems Performance Engineering

Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch. Sprache: Englisch.
kartoniert , 700 Seiten
EAN 9798341627789
Veröffentlicht 30. November 2025
Verlag/Hersteller O'Reilly Media
84,50 inkl. MwSt.
vorbestellbar (Versand mit Deutscher Post/DHL)
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Beschreibung

Elevate your AI system performance capabilities with this definitive guide to unlocking peak efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering equips professionals with actionable strategies to co-optimize hardware, software, and algorithms for high-performance and cost-effective AI systems. Authored by Chris Fregly, a performance-focused engineering and product leader, this comprehensive resource transforms complex systems into streamlined, high-impact AI solutions.
Inside, you'll discover step-by-step methodologies for fine-tuning GPU CUDA kernels, PyTorch-based algorithms, and multinode training and inference systems. You'll also master the art of scaling GPU clusters for high performance, distributed model training jobs, and inference servers.
- Codesign and optimize hardware, software, and algorithms to achieve maximum throughput and cost savings - Implement cutting-edge inference strategies that reduce latency and boost throughput in real-world settings - Utilize industry-leading scalability tools and frameworks - Profile, diagnose, and eliminate performance bottlenecks across complex AI pipelines - Integrate full stack optimization techniques for robust, reliable AI system performance
Whether you're an engineer, researcher, or developer, AI Systems Performance Engineering offers a holistic roadmap for building resilient, scalable, and cost-effective AI systems that excel in both training and inference.

Portrait

Chris Fregly is a passionate performance engineer and AI product leader with a proven track record of driving innovation at leading tech companies like Netflix, Databricks, and Amazon Web Services (AWS). He's led performance-focused engineering teams that built advanced AI/ML products, scaled go-to-market initiatives, and reduced cost for large-scale generative AI and analytics workloads. He is also co-author of 2 O'Reilly books: Data Science on AWS and Generative AI on AWS - as well as the creator of the O'Reilly online course titled, "High Performance AI in Production with Nvidia GPUs".

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