Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Abstract

Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the "route" of options that a domain expert may choose to take while developing their solution.To ground our conceptual framework in the stateof-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation.The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

"There’s no way to keep up!": Diverse Motivations and Challenges Faced by Informal Learners of ML

“…Professional ML specialists usually have formal training in computer science (CS) or applied math. However, in recent years a growing number of non-ML specialists such as scientists [3,4], web developers [5], UX designers [6], hobbyists, artists, and other creative practitioners [7] are informally learning ML to apply it to their respective domains. As ML solutions are being sought in areas ranging from medicine to journalism, the population of informal learners of ML will continue to grow [8].…”