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arXiv · 1804.05892

Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities

Doris Xin·Litian Ma·Jialin Liu·Stephen Macke·Shuchen Song·Aditya Parameswaran
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Abstract

Development of machine learning (ML) workflows is a tedious process of iterative experimentation: developers repeatedly make changes to workflows until the desired accuracy is attained. We describe our vision for a "human-in-the-loop" ML system that accelerates this process: by intelligently tracking changes and intermediate results over time, such a system can enable rapid iteration, quick responsive feedback, introspection and debugging, and background execution and automation. We finally describe Helix, our preliminary attempt at such a system that has already led to speedups of up to 10x on typical iterative workflows against competing systems.

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Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran. 2018-04-16. Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities. https://arxiv.org/abs/1804.05892

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