Résumé
With the increasing adoption of machine learning (ML), the need to manage, in a tailored manner, the complete lifecycle of this new type of software arises. MLOps (Machine Learning Operations) extends DevOps to address the specific challenges of development and lifelong management of ML-powered software. Despite the increasing attention to this domain, practitioners still lack an accessible, modular architecture to support MLOps projects. To address this lack, this paper proposes SkeltyMLOps, a reference architecture designed to promote collaboration between the diverse actors involved in MLOps processes. The architecture is derived from a thorough literature review, from which we extracted and clustered, using a Large Language Model (LLM), a comprehensive list of MLOps actors and activities. These clusters were then used to guide the architectural decomposition and component design of SkeltyMLOps. The originality of our proposed reference architecture is (i) that it clearly aligns its components with the responsibilities of MLOps actors, covering the identified dimensions of MLOps processes, and (ii) mediates collaboration by orchestrating interactions between the different types of actors. This architecture provides a foundation for building collaborative ML-powered software.