A Systematic Review of Machine Learning DevOps

DevOps and Machine Learning (ML) on their own are insufficient in certain organisational context. To achieve the desired results, industries are now combining the benefits of DevOps automation and machine learning capabilities. This synergy is known as Machine Learning DevOps (ML-DevOps). However, there is currently no shared vision, strategy, or guidance across industry enterprises on how to use ML-DevOps for application development. While still in its infancy, ML-DevOps has showed some prospects to become a dynamic approach for developing and delivering secure, robust, and scalable applications. This article describes a systematic review of peer-reviewed conference papers and journal articles on ML-DevOps from 2015 to 2022 to ascertain the state-of-the-art and then identify any gaps in the current literature. Using empirical analysis, we systematically examined more than 60 studies that primarily deal with ML-DevOps (commonly referred to as MLOps). We combined meta analysis, document analysis and triangulation to map how ML-DevOps is currently understood and how it compares and differs from related approaches such as DevOps. The articles used were accessed via credible scholarly databases. The results of our search strategy retrieved articles that primarily discuss the topics of ML and DevOps synergy. The review of the existing literature shows a paucity of studies that strictly investigate ML-DevOps/MLOps. In most cases, the focus of these studies is on clarifying the meaning of MLOps. Our study discovered that although there are common elements in the definitions of MLOps, there remains a lack of standardisation on how it is defined, which sometimes cause some misconceptions and ambiguity. Finally, although there is significant literature written on MLOps, the review did not find evidence that there is a common understanding amongst scholars and experts on how MLOps should be implemented and institutionalized across industry to create a common vision.