The PHOTON Wizard -- Towards Educational Machine Learning Code Generators
This addresses the educational gap for applied-science practitioners who need programming skills to implement machine learning solutions, though it appears incremental as it builds on existing GUI and code generation concepts.
The paper tackles the problem of applying machine learning in applied sciences hindered by lack of coding skills, by introducing the PHOTON Wizard, an open-source web application that dynamically translates GUI interactions into Python code for the PHOTON framework, enabling users to gain insights into model development workflows.
Despite the tremendous efforts to democratize machine learning, especially in applied-science, the application is still often hampered by the lack of coding skills. As we consider programmatic understanding key to building effective and efficient machine learning solutions, we argue for a novel educational approach that builds upon the accessibility and acceptance of graphical user interfaces to convey programming skills to an applied-science target group. We outline a proof-of-concept, open-source web application, the PHOTON Wizard, which dynamically translates GUI interactions into valid source code for the Python machine learning framework PHOTON. Thereby, users possessing theoretical machine learning knowledge gain key insights into the model development workflow as well as an intuitive understanding of custom implementations. Specifically, the PHOTON Wizard integrates the concept of Educational Machine Learning Code Generators to teach users how to write code for designing, training, optimizing and evaluating custom machine learning pipelines.