Auto Machine Learning (Auto-ML) has emerged as a promising solution to automate the traditional machine learning workflow. Auto-ML aims to reduce the manual intervention required in designing, training, and deploying machine learning models. This article theoretically assesses the existing Auto-ML methodologies, their benefits, limitations, and future research directions. We investigate the advantages of Auto-ML in terms of reducing human effort, increasing model accuracy, and democratizing machine learning.However, we also discuss the constraints such as the limited interpretability of Auto-ML models and the potential risk of over-reliance on automated techniques. Furthermore, we highlight the research gaps in Auto-ML, including the need for explainable Auto-ML models, personalized Auto-ML, and more efficient hyper-parameter optimization algorithms. Overall, this article provides a comprehensive review of Auto-ML techniques and serves as a roadmap for future research in this area.

Auto-ML, machine learning, benefits, limitations, future research, accuracy, interpretability, democratizing, human effort, over-reliance, explainable, personalized, hyper-parameter optimization.