Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review

Abstract:Context. Despite the growing adoption of Machine Learning Operations (MLOps), teams often approach MLOps projects in an ad hoc manner due to the lack of consolidated architectural guidance. The community would benefit from a reference that synthesizes knowledge to inform the architectural design of MLOps systems, especially regarding the integration and deployment of ML models. Objective. In response, our goal is to provide a comprehensive overview of architecturally significant guidelines for the integration and deployment of ML models in MLOps systems. Method. We conduct a gray literature review of 103 web sources to analyze state-of-practice knowledge on MLOps model integration and deployment. We then apply thematic analysis to synthesize these practices into recommended guidelines. Results. We contribute a collection of 25 architecturally significant MLOps guidelines for model integration and deployment, organized into five categories, and describe their impact on the overall system architecture. Conclusion. Our results serve as an overview of state-of-practice MLOps guidelines to support researchers and practitioners with the integration and deployment of ML models in their MLOps systems.

Software Engineering (cs.SE); Machine Learning (cs.LG)

arXiv:2606.06535 [cs.SE]

(or arXiv:2606.06535v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2606.06535 Focus to learn more arXiv-issued DOI via DataCite

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