The Past, Present, and Future of Research on the Continuous Development of AI

Abstract

Since 2020, 33 literature reviews have systematically synthesized research on the continuous development of AI, also known as Machine Learning Operations (MLOps), reflecting the increasing prevalence of AI models across various fields and the multifaceted challenges in their development, integration, and deployment. Yet, the lack of comprehensive analysis of these literature reviews and their covered topics complicates selecting relevant ones and anticipating future trends and research. In addition, these literature reviews gathered related 1397 primary sources to describe aspects of AI's continuous development, integration, and deployment, posing a hidden gem to gain insights into the past and present work and derive insights into the future of AI's continuous development. With this work, we 1) systematically collected and summarised 33 literature reviews via a Multivocal Literature Review (MLR) that focus on the continuous development, deployment, and integration of AI models. 2) Due to minimal overlap between the literature reviews' primary sources, we offer holistic insights into and interrelations of frequently addressed topics. These topics encompass the AI development pipeline, respective Software Engineering (SE) practices, and associated challenges. 3) We discuss future research directions for AI's continuous development, integration, and deployment. Therefore, we base our arguments on identified clusters in the primary sources of literature reviews. This discussion focuses on AI model reliability and resource consumption, emphasizing the interrelation of proposed future work and the effects on the whole pipeline.