Year
Month
(Preprint) Data Pricing in Machine Learning Pipelines
Zicun Cong ¹, Xuan Luo ¹, Pei Jian 裴健 ¹, Feida Zhu 朱飞达 ², Yong Zhang ³
¹ Simon Fraser University, Burnaby, Canada
² Singapore Management University, Singapore
³ Huawei Technologies Canada, Burnaby, Canada
arXiv, 2021-08-18
Abstract

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data is critical and penetrating in the whole machine learning pipelines.

As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines.

To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models, and overview pricing machine learning models for end users in the step of machine learning deployment. We also discuss a series of possible future directions.
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