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

被引：54

作者：

Xin, Doris ^{[1
]}

Ma, Litian ^{[1
]}

Liu, Jialin ^{[1
]}

Macke, Stephen ^{[1
]}

Song, Shuchen ^{[1
]}

Parameswaran, Aditya ^{[1
]}

机构：

[1] Univ Illinois Urbana Champaign UIUC, Champaign, IL 61820 USA

来源：

PROCEEDINGS OF THE SECOND WORKSHOP ON DATA MANAGEMENT FOR END-TO-END MACHINE LEARNING | 2018年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1145/3209889.3209897

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 upto 10x on typical iterative workflows against competing systems.

引用

页数：4

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