A reference architecture for an orchestrated collaborative MLOps: A ground-up design from literature combining human and LLM expertise
Year: 2026
Type: article
Abstract: With the increasing adoption of Machine Learning (ML), the need to manage, in a tailored manner, the end-to-end engineering of ML-powered software arises. Machine Learning Operations (MLOps) extends DevOps to address the specific challenges of the development and lifelong management of this new type of software. Despite the growing attention to this domain, an accepted, accessible, and modular reference architecture for MLOps is still missing. This paper presents SkeltyMLOps, a reference architecture aimed at promoting collaboration among the diverse actors involved in MLOps. SkeltyMLOps is built as rigorously as possible from knowledge gathered through a systematic literature review. First, sets of MLOps actors and activities are manually extracted from the literature by two independent authors. The extracted terms are then clustered and normalized through a process involving human and Large Language Model (LLM) experts. To carefully build the artificial expertise, several experiments are conducted using various LLMs, with or without additional contextual knowledge and under different parameter settings. The resulting clusters and labels serve as the foundation for the design of the SkeltyMLOps architecture. There, special attention is paid to designing the orchestration of MLOps activities and to the coordination of contributions of various actors. Therefore, the originality of the contribution is twofold: (1) the careful knowledge gathering process that grounds the proposed architecture, and (2) the central role given to orchestration and coordination at the heart of the architecture.
Institutions: IMT Mines Alès, Lebanese American University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,000
OpenAlex ID: W7168031152