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

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.

to be published in SIGMOD '18 DEEM Workshop

References in corpus (3)

• Scikit-learn: Machine Learning in Python
• TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries
• Helix: Holistic Optimization for Accelerating Iterative Machine Learning

Cited by in corpus (1)

• A Survey of Human-in-the-loop for Machine Learning