Symphony: Composing Interactive Interfaces for Machine Learning

TL;DR

Symphony is a framework for creating reusable, interactive machine learning interfaces that facilitate cross-team communication, discovery of data issues, and sharing insights across platforms.

Contribution

It introduces Symphony, a novel framework for composing task-specific, data-driven ML interfaces that are reusable and platform-agnostic, enhancing collaboration and insight sharing.

Findings

Symphony enabled teams to discover data duplicates and model blind spots.

It improved cross-functional communication among ML practitioners.

Symphony was successfully deployed in three production ML projects at Apple.

Abstract

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.