  AutoML systems can speed up routine data science work and make machine
learning available to those without expertise in statistics and computer
science. These systems have gained traction in enterprise settings where pools
of skilled data workers are limited. In this study, we conduct interviews with
29 individuals from organizations of different sizes to characterize how they
currently use, or intend to use, AutoML systems in their data science work. Our
investigation also captures how data visualization is used in conjunction with
AutoML systems. Our findings identify three usage scenarios for AutoML that
resulted in a framework summarizing the level of automation desired by data
workers with different levels of expertise. We surfaced the tension between
speed and human oversight and found that data visualization can do a poor job
balancing the two. Our findings have implications for the design and
implementation of human-in-the-loop visual analytics approaches.
