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.

翻译：背景。尽管机器学习运维（MLOps）的采用日益增长，但由于缺乏整合性的架构指导，团队在实施MLOps项目时常常采用临时性方法。学界与业界需一份综合知识参考，以指导MLOps系统的架构设计，特别是针对ML模型的集成与部署。目标。为此，我们的目标是提供关于MLOps系统中ML模型集成与部署的架构重要性指南的全面综述。方法。我们通过灰色文献综述方法，对103个网络来源进行分析，梳理MLOps模型集成与部署的实践知识现状。随后采用主题分析法将这些实践综合为推荐指南。结果。我们汇总了25条具有架构重要性的MLOps模型集成与部署指南，分为五大类别，并阐述了其对整体系统架构的影响。结论。我们的研究成果作为MLOps实践指南的现状综述，可支持研究人员与从业者在其MLOps系统中高效集成与部署ML模型。