In this demo, we provide a complete walkthrough of the SustainML User Interface, a framework designed to prioritize energy efficiency and reduce the carbon footprint of machine learning applications.
Whether you are a data scientist or an ML engineer, this tutorial shows you how to define problems, upload datasets, and compare models based on their environmental impact.
In this demo, we provide a complete walkthrough of the SustainML User Interface, a framework designed to prioritize energy efficiency and reduce the carbon footprint of machine learning applications.
Whether you are a data scientist or an ML engineer, this tutorial shows you how to define problems, upload datasets, and compare models based on their environmental impact.
SustainML has reached a major milestone in its mission to build a fully life-cycle-oriented, model–hardware co-design framework for sustainable machine learning: the project’s Scientific Articles portal now hosts 36 publicly available papers, including peer-reviewed publications, conference papers, technical deliverables, architectural reports, prototypes, and community-oriented outputs.
This extensive body of work collectively forms the most complete publicly available resource on Green AI engineering, covering every layer of the ML pipeline, from conceptual modelling to hardware execution, from user-centric design to carbon-footprint tracking.
The SustainML project is pleased to spotlight the publication of Sustainable AI: Tools for Moving Toward Green AI, a new book by Raghavendra Selvan that directly addresses one of SustainML’s core concerns: how to harness the power of modern AI while dramatically reducing its environmental and social footprint.
In a world already locked into dangerous levels of planetary warming, artificial intelligence has become both a promising ally and a new source of environmental pressure. Sustainable AI: Tools for Moving Toward Green AI by Raghavendra Selvan takes this tension seriously. It argues that we cannot talk about “AI for good” without also accounting for the material footprint of the AI itself – the energy, hardware, water, and labour that sit behind every model we train and deploy.
A team from the University of Copenhagen’s Department of Computer Science — Dustin Wright, Christian Igel, and Raghavendra Selvan — has introduced a novel approach to making deep learning models more efficient without compromising accuracy. Their method, called Bayesian Model Reduction for Structured Pruning (BMRS), brings a principled Bayesian perspective to one of the field’s most pressing challenges: reducing the massive computational cost of modern neural networks.
SustainML is among these nine innovative projects dedicated to creating a sustainable ML framework for Green AI.
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This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070408.
