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

D. Doris Xin, L. Litian Ma, J. Jialin Liu, S. Stephen Macke, S. Shuchen Song, A. Aditya Parameswaran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 upto 10x on typical iterative workflows against competing systems.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd Workshop on Data Management for End-To-End Machine Learning, DEEM 2018 - In conjunction with the 2018 ACM SIGMOD/PODS Conference
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450358286
DOIs
StatePublished - Jun 15 2018
Event2nd Workshop on Data Management for End-To-End Machine Learning, DEEM 2018 - In conjunction with the 2018 ACM SIGMOD/PODS Conference - Houston, United States
Duration: Jun 15 2018 → …

Publication series

NameProceedings of the 2nd Workshop on Data Management for End-To-End Machine Learning, DEEM 2018 - In conjunction with the 2018 ACM SIGMOD/PODS Conference

Conference

Conference2nd Workshop on Data Management for End-To-End Machine Learning, DEEM 2018 - In conjunction with the 2018 ACM SIGMOD/PODS Conference
CountryUnited States
CityHouston
Period6/15/18 → …

ASJC Scopus subject areas

  • Sociology and Political Science
  • Hardware and Architecture
  • Human-Computer Interaction

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