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.