Fundamentals of Cost-Efficient AI by Kumar, Rohit;

Long description:

Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine provides a comprehensive yet accessible introduction to the principles of designing, training, and deploying efficient artificial intelligence systems. It explains the theory behind cost-aware machine learning and data mining and examines methods across deep learning, graph neural networks (GNNs), transformer architectures, diffusion models, reinforcement learning, and knowledge distillation.
The book covers fine-tuning and compression techniques such as low-rank adaptation (LoRA), parameter-efficient fine-tuning (PEFT), adapter-based tuning, pruning, and quantization. It also explores inference acceleration through Flash Attention, prefill optimization, and speculative decoding, and explains how mixture-of-experts (MoE) architectures can scale models efficiently across GPUs and edge devices.
To build a strong conceptual understanding, the text introduces fundamentals of GPU architecture, matrix multiplication, memory hierarchies, and parallelization strategies, helping readers develop an intuition for optimizing training and inference pipelines.
While applicable across domains, the book places special emphasis on healthcare and biomedicine, where efficient AI can reduce costs and improve diagnostics, precision medicine, and clinical decision support. Real-world case studies and interviews with experts from organizations such as Google and Microsoft provide practical insights into building scalable healthcare AI systems. Aimed at graduate students, researchers, clinicians, biomedical engineers, data scientists, and AI practitioners, this book bridges algorithmic principles with applied implementation.

• Covers state-of-the-art techniques, including LoRA, PEFT, diffusion models, RAG, Flash Attention, and MoE architectures
• Explains methods for model compression, quantization, pruning, and knowledge distillation with practical examples
• Integrates GPU fundamentals, matrix operations, and system-level optimization for efficient model training and deployment
• Includes case studies and interviews demonstrating the use of cost-efficient AI in healthcare and biomedicine
• Balances mathematical foundations with implementation guidance and applied intuition

Table of Contents:

1. Introduction to Efficient AI Computing in Healthcare
2. Fundamentals of AI Model Efficiency in Biomedicine
3. Model Compression Techniques for Medical Data
4. Distributed Training and Parallelism in Healthcare AI
5. Gradient Compression for Efficient Medical Training
6. On-Device Optimization for Medical Devices
7. Application-Specific Efficiency in Biomedicine
8. Quantum Machine Learning and Efficiency in Biomedicine
9. Performance Optimization with PyTorch in Healthcare AI
10. Advances in Model Efficiency for Biomedicine
11. Mixture of Experts Models in Healthcare AI
12. Managing Resource Constraints in Medical AI
13. Interviews with Industry Leaders in Healthcare AI
14. Future Trends and Challenges in Healthcare AI