Eco aware Machine learning:Minimizing Environmental Footprints in Model training

Abstract

The abstract of “Eco-aware Machine Learning: Minimizing Environmental Footprints in Model Training” explores strategies to reduce the ecological impact of machine learning. This involves optimizing algorithms for efficiency, leveraging transfer learning, employing energy-efficient hardware, and adopting model compression techniques. By emphasizing data and algorithmic efficiency, along with considering renewable energy usage, the abstract underscores the importance of holistic approaches for sustainable machine learning practices. The research aims to contribute to environmentally conscious AI development by mitigating the environmental footprints associated with model training pprocesses The environmental impact of machine learning models is garnering more attention, but primarily from academics. The discussion here tends to focus on the carbon footprint of language models, which are not necessarily representative of the wider machine learning field, and there is little emphasis on the environmental impact of machine learning models’ operations phase. Furthermore, available material on the environmental impact of machine learning focuses too little on how the impact might be measured and reduced. This essay attempts to address these challenges and is intended for both practitioners and researchers who conduct hands-on machine learning. Although this essay was prepared with machine learning in mind, some of the information is equally useful for general software engineering.

DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.138-143