Symphony: Composing Interactive Interfaces for Machine Learning
This work addresses the problem of poor communication and collaboration in cross-functional ML teams, offering a practical solution for composing reusable interfaces, though it is incremental in building on existing interface concepts.
The authors tackled the limited adoption of machine learning interfaces in practice by developing Symphony, a framework for composing interactive ML interfaces that can be reused and shared across teams, which helped practitioners discover issues like data duplicates and model blind spots in production projects at Apple.
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a framework for composing interactive ML interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards. We developed Symphony through participatory design sessions with 10 teams (n=31), and discuss our findings from deploying Symphony to 3 production ML projects at Apple. Symphony helped ML practitioners discover previously unknown issues like data duplicates and blind spots in models while enabling them to share insights with other stakeholders.