Automated Machine Learning Techniques in Prognostics of Railway Track Defects The readiness and usefulness of Automated Machine Learning (AutoML) methods in classification of railway track defects is explored. Safety of railway networks is the top priority in the railroad industry, and track defects are a common cause of train accidents and service disruptions around the world. Effective classification and prediction of these defects based on historical inspection data can help in planning maintenance activities before critical defects occur. This increases safety of the network and lowers costs of the maintenance. The experimental analysis carried out on data from an international predictive modelling competition has shown that the proposed AutoML approaches resulted in an improved performance in comparison to the competition winning solutions and have an excellent potential for building robust predictive models in railway industry.

Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and effort on repetitive tasks in ML pipelines, such as data preprocessing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis. In this paper, we investigate the current state of AutoML tools aiming to automate these tasks. We conduct various evaluations of the tools on many datasets, in different data segments, to examine their performance, and compare their advantages and disadvantages on different test cases.