Machine learning for all! Benchmarking automated, explainable, and coding-free platforms on civil and environmental engineering problems

Abstract

One of the key challenges in fully embracing machine learning (ML) in civil and environmental engineering revolves around the need for coding (or programming) experience and for acquiring ML-related infrastructure. This barrier can be overcome through the availability of various platforms that provide automated and coding-free ML services, as well as ML infrastructure (in the form of a cloud service or software). Thus, engineers can now adopt, create, and apply ML to tackle various problems with ease and little coding. From this view, this paper presents a comparison of five automated and coding-free ML platforms: BigML, Dataiku, DataRobot, Exploratory, and RapidMiner on civil and environmental engineering problems. This comparison shows that although these platforms differ in their setup, services, and provided ML algorithms, all platforms performed adequately and comparably well to each other and to coding-based ML analyses. These findings denote the usefulness of coding-free ML platforms, which can lead to a brighter future for ML integration into our domain.