Sustainable AI

In the era of big data and even bigger machine learning models powering the current generative AI revolution, the environmental footprint of these developments can no longer be ignored. This much-needed guide confronts the challenge head-on, offering a groundbreaking exploration into making deep learning (DL) both efficient and accessible. Author Raghavendra Selvan exposes the high costs—both environmental and economic—of traditional DL methods and presents practical solutions that pave the way for a more sustainable AI.

This essential read is for anyone in the machine learning field, from the academic researcher to the industry practitioner, who wants to make a meaningful impact on both their work and the world. This book enables readers to be agents of change toward a more sustainable and inclusive technological future.

• Learn strategies to significantly reduce the energy consumption, carbon footprint, and hardware demands of DL models
• Examine ways to break down barriers and foster a more inclusive future in AI development
• Explore strategies for cutting costs and minimizing ecological impact
• Learn how to balance performance with efficiency in model development
• Gain proficiency in cutting-edge tools that enhance the sustainability of your AI projects

About the Author

Raghavendra Selvan (Raghav) is currently an Assistant Professor at the University of Copenhagen, with joint responsibilities at the Machine Learning Section (Dept. of Computer Science), Kiehn Lab (Department of Neuroscience) and the Data Science Laboratory. He received his PhD in Medical Image Analysis (University of Copenhagen, 2018). Raghavendra Selvan was born in Bangalore, India. His current research interests broadly pertain to Resource Efficient ML, Medical Image Analysis with ML, Quantum Tensor Networks and Graph Neural Networks. An overarching theme of his current research interests lies at the intersection of sustainability and ML where he is interested in investigating sustainability with ML, and also the sustainability of ML.