Stanford community & AIMI affiliates only
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes it challenging for non-technical collaborators and endpoint users (e.g. physicians) to easily provide feedback on model development and to gain trust in ML. The accessibility challenge also makes collaboration more difficult and limits the ML researcher’s exposure to realistic data and scenarios that occur in the wild. To improve accessibility and facilitate collaboration, we developed an open-source Python package, Gradio, which allows researchers to rapidly generate a visual interface for their ML models. Gradio makes accessing any ML model as easy as opening a URL in your browser. Our development of Gradio is informed by interviews with a number of machine learning researchers who participate in interdisciplinary collaborations. We developed these features and carried out a case study to understand Gradio’s usefulness and usability in the setting of a machine learning collaboration between a researcher and a cardiologist.
Abubakar is a 5th year PhD student in machine learning at Stanford, supervised by Professor James Zou. His research interests include self-supervised learning, generative models, and applications of machine learning to biology and medicine.