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

Autor:	Xin, Doris, Ma, Litian, Liu, Jialin, Macke, Stephen, Song, Shuchen, Parameswaran, Aditya
Rok vydání:	2018
Předmět:	Computer Science - Databases
Druh dokumentu:	Working Paper
Popis:	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. Comment: to be published in SIGMOD '18 DEEM Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1804.05892 Zobrazit plný text záznamu View this record from Arxiv