Green AI: Reducing the Carbon Footprint of Python-Based Machine Learning
DOI:
https://doi.org/10.15680/IJCTECE.2023.0604002Keywords:
Green AI, Carbon Footprint, Machine Learning, Python, Model Optimization, Energy Efficiency, Sustainable Practices, Deep Learning, Environmental ImpactAbstract
Machine learning (ML) models, particularly deep learning architectures, have become integral to various applications. However, their environmental impact is significant, with training large models consuming substantial energy and emitting considerable carbon dioxide. This paper explores strategies to mitigate the carbon footprint of Python-based ML workflows, focusing on optimization techniques, hardware considerations, and sustainable practices. By implementing these strategies, developers can contribute to more sustainable AI development without compromising model performance.
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