Fundamentals of Cost-Efficient AI

Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine, 1st Edition, authored by Rohit Kumar and published by Academic Press, provides a practical guide to resource-conscious artificial intelligence design. The book offers graduate students, researchers, data scientists, and AI practitioners systematic approaches to managing computational costs in modern model workflows. It focuses on engineering efficient computational pipelines for complex biomedical applications.

The technical coverage synthesizes essential compression and fine-tuning techniques, including Low-Rank Adaptation (LoRA), parameter-efficient fine-tuning (PEFT), adapter-based tuning, pruning, quantization, and knowledge distillation. To optimize model execution, the text examines inference acceleration methods like Flash Attention, prefill optimization, and speculative decoding. Additionally, it addresses efficient reinforcement learning and graph neural networks to broaden algorithmic efficiency.

A defining aspect of the text is its application to healthcare and biomedicine, highlighting diagnostic systems, precision medicine research, and clinical decision support tools. It provides biomedical engineers and clinicians with targeted computational strategies for deploying performant models under strict hardware limitations.