Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
Pohsun Feng, Ziqian Bi, Yizhu Wen, Benji Peng, Junyu Liu, Caitlyn Heqi Yin, Tianyang Wang, Keyu Chen, Sen Zhang, Ming Li, Jiawei Xu, Ming Liu, Xuanhe Pan, Jinlang Wang, Xinyuan Song, Qian Niu
TL;DR
The paper surveys Automated Machine Learning (AutoML) across the ML spectrum, from foundational Python and ML concepts to scalable cloud-based tools. It details multiple AutoML platforms (TPOT, AutoGluon, Auto-Keras, Auto-sklearn, FLAML, DataRobot, H2O AutoML, etc.), as well as algorithmic families (SVM, trees, boosting, sparse models) and advanced topics like NAS and AutoDL frameworks. It also discusses practical workflows, hyperparameter tuning, parallel and GPU-enabled processing, and cloud/remote computing resources, highlighting benefits, limitations, and deployment considerations. The work emphasizes AutoML’s potential to democratize AI, accelerate model development, and support deployment while recognizing the need for human oversight, interpretability, and responsible use. Overall, the document serves as a comprehensive guide for practitioners ranging from beginners to advanced users to navigate the AutoML landscape and leverage state-of-the-art techniques in real-world settings.
Abstract
A comprehensive guide to Automated Machine Learning (AutoML) is presented, covering fundamental principles, practical implementations, and future trends. The paper is structured to assist both beginners and experienced practitioners, with detailed discussions on popular AutoML tools such as TPOT, AutoGluon, and Auto-Keras. Emerging topics like Neural Architecture Search (NAS) and AutoML's applications in deep learning are also addressed. It is anticipated that this work will contribute to ongoing research and development in the field of AI and machine learning.