构建AI应用运维框架，解决数据驱动项目中的持续监控与协作难题
AI Application Operations -- A Socio-Technical Framework for Data-driven Organizations
- 以真实组织经验为基础，设计从想法到上线的全流程框架
- 将监控作为反馈机制，实现持续改进与合规性保障
- 适合正在推进AI落地或优化运维流程的组织参考
本文基于多个组织的真实实践经验，提出一套完整的人工智能应用运维（AIAppOps）框架。数据驱动项目因在开发与运维全周期中高度依赖数据，带来额外挑战。为应对这些挑战，框架明确了从创意到生产的关键步骤与角色分工。由于数据依赖性，各环节均可能出现偏差，因此框架将监控不仅视为防护手段，更作为统一的反馈机制，推动持续改进、合规性和价值持续实现，结合统计与形式化保证方法，拓展了安全关键AI的运行时验证理念至组织运营层面。框架涵盖核心技术流程与支持服务，既可指导新项目启动，也适用于成熟AI项目的演进。
原文摘要 · Abstract (English)
We outline a comprehensive framework for artificial intelligence (AI) Application Operations (AIAppOps), based on real-world experiences from diverse organizations. Data-driven projects pose additional challenges to organizations due to their dependency on data across the development and operations cycles. To aid organizations in dealing with these challenges, we present a framework outlining the main steps and roles involved in going from idea to production for data-driven solutions. The data dependency of these projects entails additional requirements on continuous monitoring and feedback, as deviations can emerge in any process step. Therefore, the framework embeds monitoring not merely as a safeguard, but as a unifying feedback mechanism that drives continuous improvement, compliance, and sustained value realization-anchored in both statistical and formal assurance methods that extend runtime verification concepts from safety-critical AI to organizational operations. The proposed framework is structured across core technical processes and supporting services to guide both new initiatives and maturing AI programs.
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