Machine Learning-Enabled Software and System Architecture Frameworks

arXiv:2308.05239v2 Announce Type: replace-cross
Abstract: Various architecture frameworks for software, systems, and enterprises have been proposed in the literature. They identified several stakeholders and defined modeling perspectives, architecture viewpoints, and views to frame and address stakeholder concerns. However, the stakeholders with data science and Machine Learning (ML) related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks. Only this way can we envision a holistic system architecture description of an ML-enabled system. …

abstract architecture arxiv concerns cs.lg cs.se data data science data scientists enterprises frameworks however literature machine machine learning modeling perspectives replace science scientists software stakeholder stakeholders system architecture systems type