Overview

""Sustainable Green AI"" is a comprehensive and practical guide dedicated to the principles, design, and implementation of environmentally responsible Artificial Intelligence. It addresses a critical gap in technology education and practice: the urgent need to mitigate the substantial energy and carbon footprint of modern AI systems. This book provides a complete roadmap for students, educators, and industry professionals to build AI solutions that are both computationally powerful and ecologically sustainable. Philosophy The core philosophy of this book is that efficiency is the cornerstone of responsible innovation. For too long, the pursuit of state-of-the-art performance in AI has overshadowed concerns about its environmental impact. I contend that sustainability is not a trade-off against performance but a parallel goal that drives better engineering, more elegant models, and more thoughtful deployment. Green AI is not about limiting AI's capabilities; it is about achieving those capabilities with minimal environmental cost. Key Features 1. Industry & Academic Alignment: The curriculum is meticulously designed to align with the syllabi of B.Tech and M.Tech Computer Science programs at leading universities in the USA and internationally, while also meeting the practical skill requirements of the tech industry. 2. Application-Oriented: The text heavily emphasizes the practical application of Green AI principles. The focus is always on building real solutions, not just understanding theories. 3. Comprehensive Coverage: From efficient data handling and lightweight model architectures to energy-aware hardware and carbon-aware cloud deployment, the book covers the entire spectrum of Sustainable AI. 4. Simple and Accessible: Concepts are explained in the simplest possible terms, with clear analogies and practical examples to make them understandable for readers at all levels. 5. Up-to-Date Content: The book covers the latest trends and techniques in the field, including federated learning, model quantization, pruning, and the use of specialized AI accelerators. Key Takeaways Upon completing this book, you will be able to: 1. Analyze the Environmental Impact of AI: Understand and quantify the energy consumption and carbon footprint of AI models and workflows. 2. Design Efficient AI Models: Master techniques like model pruning, quantization, and knowledge distillation to create smaller, faster, and less energy-intensive models. 3. Implement Sustainable MLOps: Apply Green AI principles to the entire machine learning lifecycle, from data acquisition to model deployment and monitoring. 4. Leverage Hardware and Cloud for Efficiency: Select the right hardware (CPUs, GPUs, TPUs) and configure cloud resources for optimal energy efficiency. 5. Develop Carbon-Aware Applications: Build AI systems that can adapt their behavior based on the availability of renewable energy. 6. Build an End-to-End Green AI Project: Apply your skills to a complete capstone project, developing a functional, sustainable AI application from scratch. Disclaimer: Earnest request from the Author. Kindly go through the table of contents and refer kindle edition for a glance on the related contents. Thank you for your kind consideration!

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