Constructing reusable knowledge for machine learning projects based on project practices
This study focuses on projects for the development of ML-based service systems in which ML techniques are applied to enterprise functions and proposes a method for collecting insights by referring to a development model based on project practices and developing patterns for ML projects as an enterprise architecture model.
Abstract
Recently, machine learning (ML) techniques have been introduced into various domains. This study focuses on projects for the development of ML-based service systems in which ML techniques are applied to enterprise functions. In these projects, constructing reusable knowledge on projects that develop ML-based service systems is important to effectively implement such projects. Here, the collection of insights and development of architecture and design patterns for ML-based service systems are considered. We propose a method for collecting insights by referring to a development model based on project practices and developing patterns for ML projects as an enterprise architecture model. Through a practice, we attempt to collect insights as best practices and construct design patterns for ML projects using the proposed method.