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

Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows