  The development of Machine Learning (ML) based systems is complex and
requires multidisciplinary teams with diverse skill sets. This may lead to
communication issues or misapplication of best practices. Process models can
alleviate these challenges by standardizing task orchestration, providing a
common language to facilitate communication, and nurturing a collaborative
environment. Unfortunately, current process modeling languages are not suitable
for describing the development of such systems. In this paper, we introduce a
framework for modeling ML-based software development processes, built around a
domain-specific language and derived from an analysis of scientific and gray
literature. A supporting toolkit is also available.
