Artifact and reference models for generative machine learning frameworks and build systems

Abstract

Machine learning is a discipline which has become ubiquitous in the last few years. While the research of machine learning algorithms is very active and continues to reveal astonishing possibilities on a regular basis, the wide usage of these algorithms is shifting the research focus to the integration, maintenance, and evolution of AI-driven systems. Although there is a variety of machine learning frameworks on the market, there is little support for process automation and DevOps in machine learning-driven projects. In this paper, we discuss how metamodels can support the development of deep learning frameworks and help deal with the steadily increasing variety of learning algorithms. In particular, we present a deep learning-oriented artifact model which serves as a foundation for build automation and data management in iterative, machine learning-driven development processes. Furthermore, we show how schema and reference models can be used to structure and maintain a versatile deep learning framework. Feasibility is demonstrated on several state-of-the-art examples from the domains of image and natural language processing as well as decision making and autonomous driving.