A Comprehensive AutoML Solution for Automated Data Preprocessing and Model Deployment

Abstract

An important turning point in the field of machine learning has been reached with the convergence of data preparation and automated machine learning (AutoML). AutoML has become a reliable solution for tackling major issues with data preprocessing approaches because of its capacity to automate the coordination of different machine learning processes. This study covers a wide range of important topics related to data preparation, including feature selection, timeseries preprocessing, manual encoding mistakes, class imbalance, and inefficient hyperparameters. AutoML's revolutionary effect on simplifying crucial data preparation procedures is one of its main contributions to data preprocessing. Data preparation has historically been a labor-and time-intensive procedure that calls for specialised knowledge and physical involvement at different points in the process. But many of these jobs may now be completed automatically because to the development of automated algorithms, which has significantly increased productivity and efficiency. Furthermore, by making data preprocessing more approachable for both specialists and non-experts, AutoML has democratised the field. Through the automation of intricate processes like feature selection and hyperparameter tweaking, AutoML technologies enable users to concentrate on more advanced parts of model creation, such formulating problems and interpreting outcomes. In addition to quickening the rate of invention, this democratisation of data preprocessing encourages increased cooperation and knowledge exchange within the machine learning community.