MLOps in Practice: Requirements and a Reference Architecture from Industry
Kumara,Indika Arts,Rowan Ferreira,Renato Cordeiro Nucci,Dario Di Kazman,Rick Tamburri,Damian Andrew van den Heuvel,Willem-Jan
Kumara,Indika
Arts,Rowan
Ferreira,Renato Cordeiro
Nucci,Dario Di
Kazman,Rick
Tamburri,Damian Andrew
van den Heuvel,Willem-Jan
Abstract
Machine Learning Operations (MLOps) streamline the lifecycle of machine learning (ML) models in production. In recent years, the topic has attracted the interest of practitioners, and consequently, a considerable number of tools and gray literature on architecting MLOps environments have emerged. However, this has created a new problem for organizations: selecting the most appropriate tools and design options to implement their MLOps environments. To alleviate this problem, this paper proposes a reference architecture and 32 requirements for MLOps by systematically reviewing 59 articles in the industrial gray literature. Furthermore, we used a survey and conducted semi-structured interviews with six MLOps experts to validate, refine, and extend our findings. This reference architecture, derived from the current state of practice, will enable organizations to make informed design and technology choices when embarking on their MLOps journey, while providing a technology-independent baseline for further MLOps research.
Description
Date
2025-08-20
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
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Keywords
MLOps, Machine Learning Operations, Requirements, Reference Architecture, Gray Literature, Interviews
Citation
Kumara, I, Arts, R, Ferreira, R C, Nucci, D D, Kazman, R, Tamburri, D A & van den Heuvel, W-J 2025, MLOps in Practice: Requirements and a Reference Architecture from Industry. in Software Architecture : 19th European Conference, ECSA 2025, Limassol, Cyprus, September 15–19, 2025, Proceedings. vol. 15929, Lecture Notes in Computer Science, vol. 15929, pp. 20-37. https://doi.org/10.1007/978-3-032-02138-0_2