AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch
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About this product
Overview
This comprehensive guide tackles the critical challenge of optimizing AI model performance across training and inference pipelines. It combines practical GPU acceleration techniques with real-world engineering strategies for production environments.
Key Specifications
The book covers CUDA programming, PyTorch optimization, and GPU memory management across approximately 400+ pages of technical content. It includes hands-on examples, performance benchmarking methodologies, and detailed case studies from enterprise deployments.
Who It's For
Machine learning engineers scaling models to production and data scientists frustrated with slow training times will find immediate value in the optimization patterns presented. It's ideal for teams building inference services where latency and throughput directly impact business metrics.
Worth Buying?
The depth of GPU utilization strategies and distributed training techniques makes this essential for anyone serious about model performance beyond basic implementations. The practical focus on bottleneck identification and systematic optimization justifies the investment for professionals working with large-scale AI systems.