Automating Machine Learning (AutoML) in Python: Efficiency Meets Sustainability
DOI:
https://doi.org/10.15680/3csbpy79Keywords:
Automated Machine Learning (AutoML), Python, Energy Efficiency, Sustainability, Green AI, Hyperparameter Optimization, Model Selection, Feature EngineeringAbstract
Automated Machine Learning (AutoML) has revolutionized the field of machine learning by automating the process of model selection, hyperparameter tuning, and feature engineering, thereby enhancing efficiency and accessibility. However, the computational demands of AutoML can lead to significant energy consumption and environmental impact. This paper explores the integration of sustainability principles into AutoML practices, focusing on Python-based frameworks. By examining existing literature and proposing strategies for energy-efficient AutoML, we aim to contribute to the development of environmentally responsible machine learning solutions.
References
1. Tornede, T., Tornede, A., Hanselle, J., Wever, M., Mohr, F., & Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions. arXiv.arXiv
2. Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., & Smola, A. (2020). AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. arXiv.arXiv
3. Brownlee, J. (2020). Automated Machine Learning (AutoML) Libraries for Python. Machine Learning Mastery.MachineLearningMastery.com
4. Yogi, A. (2021). 14 Python AutoML Frameworks Data Scientists Can Use. Analytics Yogi.Analytics Yogi
5. Bloggers, P. (2023). Unleashing AutoML: Revolutionizing Machine Learning Efficiency. Python-bloggers.Python-bloggers
