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ABSTRACT
The growing integration of artificial intelligence (AI) into everyday life necessitates a transformation in machine learning (ML) education and development practices, empowering end users with domain knowledge to independently design, train, test, and deploy specialized ML models. However, the technical complexity of ML, particularly in areas such as neural networks, presents a significant barrier for those users. To overcome this challenge, it is essential to reduce the cognitive burden associated with coding, algorithm configuration, and system setup. This study introduces an early-stage prototype of an open-source, cloud-based visual ML platform aimed at lowering this barrier. The platform enables users to configure, execute, and monitor ML workflows through an intuitive graphical interface, eliminating the need for programming skills or environment setup. To evaluate the platform's usability and user-friendliness, a user study was conducted involving participants from diverse academic backgrounds. Participants engaged with both visual and command-line versions of the system and completed a structured questionnaire. The results revealed a strong preference for the visual interface, especially among users with limited technical experience. These findings suggest that intuitive, no-code platforms can significantly reduce entry barriers and foster broader engagement with ML in educational settings.
Keywords: Machine learning, Visual inquiry tool, Cloud platform, User friendliness
1. INTRODUCTION
Machine Learning (ML) has emerged as an essential pillar of today's digital landscape, exerting a profound influence across numerous fields. Traditionally, the development and application of ML models have been in the purview of computer science (Mason, 2013). Computer scientists are technically proficient but usually lack knowledge of the intricacies of the specific areas to which models are applied (Berthold, 2019; Luna-Reyes, 2018; Schreck & Veeramachaneni, 2016). Therefore, collaborative work has always existed between computer scientists and people in respective domains, such as information systems and data science. However, the wide-spread adoption of ML across different fields underscores the need for a paradigm that places domain practitioners at the core when it comes to designing, training, testing, and deploying a customized ML model (Sundberg & Holmstrom, 2024; Yang et al., 2018). This paradigm shift presents a significant challenge, particularly in the context of ML education (Topi, 2019; Wang & Wang, 2021). Hence, there is a need for an abstraction or virtualization layer that conceals ML complexities from users, enabling...