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.
@article{arxiv.1804.05892,
title = {Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities},
author = {Doris Xin and Litian Ma and Jialin Liu and Stephen Macke and Shuchen Song and Aditya Parameswaran},
journal= {arXiv preprint arXiv:1804.05892},
year = {2018}
}