Enterprise architecture-based metamodel for machine learning projects and its management

In this study, we consider projects for developing service systems using machine learning (ML) techniques. These projects involve collaboration between various stakeholders. Several types of models representing system architectures are introduced so that stakeholders can develop a common understanding of these projects. In addition, metamodels are constructed by combining ML service systems models to provide project practitioners with a holistic view of the projects. In certain cases, these metamodels need to be extended to incorporate other business models for the business-IT alignment used in enterprises. For such situations, an enterprise architecture-based metamodel and method for managing the metamodel are proposed in this study, which provide a holistic view of business-IT alignment for ML projects. We confirm the effectiveness of the proposed metamodel and management method through real examples.