Chris Fregly

AI Systems Performance Engineering

Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 500
• Erscheinungstermin: 4. November 2025
• Englisch
• ISBN-13: 9798341627789
• Artikelnr.: 73860497

Autorenporträt

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".