Building a Reproducible Machine Learning Pipeline

Understand

Reproducibility of modeling is a problem that exists for any machine learning practitioner, whether in industry or academia.

• The consequences of an irreproducible model can include significant financial costs, lost time, and even loss of personal reputation (if results prove unable to be replicated).
• This paper will first discuss the problems we have encountered while building a variety of machine learning models, and subsequently describe the framework we built to tackle the problem of model reproducibility.
• The framework is comprised of four main components (data, feature, scoring, and evaluation layers), which are themselves comprised of well defined transformations.