Email this article Sustainable Machine Learning: Enhancing Efficiency and Environmental Impact
Abstract
The abstract investigates the pivotal role of Sustainable Machine Learning (SML) in enhancing efficiency while mitigating the environmental impact of computational systems. SML focuses on optimizing algorithms, hardware, and practices to reduce energy consumption and carbon footprint. This abstract explores various approaches such as model compression, resource-aware algorithms, and energy-efficient hardware design that contribute to the sustainability of machine learning frameworks. It highlights the significance of eco-friendly computing in meeting the increasing computational demands while minimizing environmental degradation. Additionally, the abstract discusses the challenges and future directions in SML, envisioning a greener and more efficient machine learning landscape that aligns technological advancements with ecological sustainability.
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