A Framework to Model ML Engineering Processes

arXiv:2404.18531v1 Announce Type: cross
Abstract: 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 …

abstract arxiv best practices challenges communication cs.ai cs.lg cs.se development diverse engineering framework language machine machine learning orchestration practices process processes skill systems teams type