  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.
