  The task of developing a machine learning (ML) model for a particular problem
is inherently open-ended, and there is an unbounded set of possible solutions.
Steps of the ML development pipeline, such as feature engineering, loss
function specification, data imputation, and dimensionality reduction, require
the engineer to consider an extensive and often infinite array of
possibilities. Successfully identifying high-performing solutions for an
unfamiliar dataset or problem requires a mix of mathematical prowess and
creativity applied towards inventing and repurposing novel ML methods. Here, we
explore the feasibility of hosting crowdsourced ML challenges to facilitate a
breadth-first exploration of open-ended research problems, thereby expanding
the search space of problem solutions beyond what a typical ML team could
viably investigate. We develop MLPro, a system which combines the notion of
open-ended ML coding problems with the concept of an automatic online code
judging platform. To conduct a pilot evaluation of this paradigm, we
crowdsource several open-ended ML challenges to ML and data science
practitioners. We describe results from two separate challenges. We find that
for sufficiently unconstrained and complex problems, many experts submit
similar solutions, but some experts provide unique solutions which outperform
the "typical" solution class. We suggest that automated expert crowdsourcing
systems such as MLPro have the potential to accelerate ML engineering
creativity.
