ml engineering

The book addresses the operational knowledge that is essential for large-scale ML work but rarely covered in academic ML education: GPU cluster management, distributed training debugging, memory optimization, mixed-precision training pitfalls, and making the most of expensive compute budgets.

Content covers GPU hardware selection and benchmarking, network interconnect requirements for multi-node training, distributed training frameworks and their failure modes, debugging techniques for training instabilities and divergence, data pipeline optimization to avoid compute bottlenecks, checkpoint management strategies, and the operational knowledge needed to run training jobs that cost tens or hundreds of thousands of dollars reliably.

The book is written from hands-on experience with actual production training runs rather than from theoretical understanding alone.

ML engineers and infrastructure teams preparing to train large models on multi-GPU and multi-node clusters, practitioners transitioning from research-scale to production-scale training, and organizations building the internal capability to train foundation models use ML Engineering as a practical reference.

The book fills a significant gap in available resourcesmost ML education focuses on model architecture and algorithms, while the engineering challenges of actually running large-scale training are scattered across blog posts, Discord channels, and tribal knowledge within organizations that have done it before.