Reusable, explorable, and sharable data science components

Symphony is a framework for composing interactive machine learning interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards.

This code accompanies the following research paper:

Symphony: Composing Interactive Interfaces for Machine Learning
Alex Bäuerle*, Ángel Alexander Cabrera*, Fred Hohman, Megan Maher,
David Koski, Xavier Suau, Titus Barik, Dominik Moritz
ACM Conference on Human Factors in Computing Systems (CHI), 2022.
Paper, Code, Preview, Video *Contributed equally


Check out the Getting Started page to install and use Symphony, or browse our Examples to get an idea of the types of analyses Symphony enables.

If you want to create your own component, check out our Creating a Component page, or explore the Symphony code at our GitHub repo.

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