  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.
