Abstract
Machine Learning (ML) has grown in popularity in the software industry
due to its ability to solve complex problems. Developing ML Systems
involves more uncertainty and risk because it requires identifying a
business opportunity and managing the source code, data, and trained
model. Our research aims to identify the existing practices used in the
industry for building ML applications. The goal is to comprehend the
orga- nizational complexity of adopting ML Systems. We conducted a
Multivocal Literature Review (MLR) and used Grounded Theory (GT) to
build a taxonomy with the practices applied to the ML System lifecycle
from the industry and academic perspectives. We selected 41 posts from
grey literature and 37 papers from scientific repositories. Following a
systematic GT protocol, we mapped 91 practices, grouped in 6 core
categories related to designing, developing, testing, and deploying ML
Systems. The results can help organizations identify the gaps in their
current ML processes and practices, and provide a roadmap for improving
and optimizing their ML systems. The comprehensive taxonomy of practices
developed in this research serves as a valuable tool for managers,
practitioners, and researchers in the ML field, providing a clear and
organized understanding of the complexity of managing ML systems.