  Efforts to make machine learning more widely accessible have led to a rapid
increase in Auto-ML tools that aim to automate the process of training and
deploying machine learning. To understand how Auto-ML tools are used in
practice today, we performed a qualitative study with participants ranging from
novice hobbyists to industry researchers who use Auto-ML tools. We present
insights into the benefits and deficiencies of existing tools, as well as the
respective roles of the human and automation in ML workflows. Finally, we
discuss design implications for the future of Auto-ML tool development. We
argue that instead of full automation being the ultimate goal of Auto-ML,
designers of these tools should focus on supporting a partnership between the
user and the Auto-ML tool. This means that a range of Auto-ML tools will need
to be developed to support varying user goals such as simplicity,
reproducibility, and reliability.
