A Framework to Model ML Engineering Processes
This addresses communication and standardization issues in multidisciplinary ML teams, though it appears incremental as it builds on existing process modeling concepts.
The authors tackled the complexity of developing machine learning systems by introducing a framework with a domain-specific language for modeling ML engineering processes, derived from literature analysis and accompanied by a toolkit.
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.