Paper Contents
Abstract
Machine learning (ML) technology has progressed at a rate that has resulted in an exponential increase in the requirements for computing. It has brought to the forefront questions regarding the environments in which these learning models have to be constructed. This paper discusses sustainable approaches to reduce energy consumption while retaining high performance and accuracy standards. We analyze the environmental guidance of ML pipelines by focusing on labor-intensive tasks such as hyperparameter tuning, preprocessing and model training. In this case of techniques such as pattern pruning, quantization, transfer learning, and energy saving techniques, it has been possible to reduce carbon emissions. We have also emphasized the role of renewable energy and the best data spaces that can support AI application. Data from research in optimizing the training of large-scale language models and neural networks that it is possible to cut power consumption without a reduction in performance. This research integrates sustainability into the design of machine learning models and point out the importance of developing knowledge about AI technologies to address the increasing challenges in the digital age environment.
Copyright
Copyright © 2025 Haider Abass. This is an open access article distributed under the Creative Commons Attribution License.
