Reducing Carbon Emissions in Model Training: Eco-Conscious Machine Learning

Abstract

As the demand for computational resources in machine learning continues to grow, so too does the environmental impact associate with model training. This paper addresses the urgent need for eco-conscious practices in machine learning by proposing strategies to reduce carbon emissions during the training process. We present a comprehensive review of the environmental footprint of model training and explore various techniques to minimize energy consumption and carbon emissions. These include algorithmic optimizations, hardware efficiency improvements, and renewable energy utilization. Through empirical evaluations and case studies, we demonstrate the feasibility and effectiveness of eco-conscious machine learning approaches in reducing carbon emissions without compromising model performance. This work contributes to the development of sustainable machine learning practices and promotes the adoption of environmentally responsible methodologies in the AI community.

DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.149-153