Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
This work addresses the problem of inefficient ML workflow development for practitioners, offering insights into optimizing iteration strategies, though it is incremental as it builds on existing data analysis.
The study analyzed over 475,000 user-generated machine learning workflows on OpenML to understand how people iterate on ML workflows in practice, finding that automated approaches explore more options and achieve higher performance, while manual approaches waste fewer iterations.
It is well-known that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model. Users currently rely on empirical trial-and-error to obtain their own set of battle-tested guidelines to inform their modeling decisions. In this study, we aim to demystify this dark art by understanding how people iterate on ML workflows in practice. We analyze over 475k user-generated workflows on OpenML, an open-source platform for tracking and sharing ML workflows. We find that users often adopt a manual, automated, or mixed approach when iterating on their workflows. We observe that manual approaches result in fewer wasted iterations compared to automated approaches. Yet, automated approaches often involve more preprocessing and hyperparameter options explored, resulting in higher performance overall--suggesting potential benefits for a human-in-the-loop ML system that appropriately recommends a clever combination of the two strategies.