MLOps: Practices, Maturity Models, Roles, Tools, and Challenges - A Systematic Literature Review

Abstract

Context: The development of machine learning solutions has increased significantly due to the advancement of technology based on artificial intelligence. MLOps have emerged as an approach to minimizing efforts and improving integration between those who are in the process of deploying the models in the production environment. Objective: This paper undertakes a systematic literature review in order to identify practices, standards, roles, maturity models, challenges, and tools related to MLOps. Method: The study is founded on an automatic search method of selected digital libraries that applies selection and quality criteria to identify suitable papers that underpin the research. Results: The search initially found 1,905 articles of which 30 papers were selected for analysis. This analysis led to findings that made it possible to achieve the objectives of the research. Conclusion: The results allowed us to conclude that MLOps is still in its initial stage, and to recognize that there is an opportunity to undertake further academic studies that will prompt organizations to adopt MLOps practices.