扩展企业推荐系统以实现去中心化
Scaling Enterprise Recommender Systems for Decentralization
摘要：
在去中心化的组织中，对推荐系统的本地需求不断增长，以支持业务流程。数据源和基础设施的多样性给中央工程团队带来了挑战。实现高交付速度，没有技术债务，需要一个可扩展的方法在推荐系统的开发和操作。在喜力公司，我们执行一种机器学习操作方法，其中有五个最佳实践：管道自动化、数据可用性、可交换工件、可观察性和基于策略的安全性。创建自助服务、自动化和协作的文化，以扩展推荐系统，实现去中心化。我们展示了一个自助式ML工作区部署和推荐系统的实际用例，可以更快地扩展到子公司，并且技术债务更少。这使喜力能够在全球范围内支持那些能够产生具有本地业务影响的见解的应用程序。
英文摘要：
Within decentralized organizations, the local demand for recommender systems to support business processes grows. The diversity in data sources and infrastructure challenges central engineering teams. Achieving a high delivery velocity without technical debt requires a scalable approach in the development and operations of recommender systems. At the HEINEKEN Company, we execute a machine learning operations method with five best practices: pipeline automation, data availability, exchangeable artifacts, observability, and policy-based security. Creating a culture of self-service, automation, and collaboration to scale recommender systems for decentralization. We demonstrate a practical use case of a self-service ML workspace deployment and a recommender system, that scale faster to subsidiaries and with less technical debt. This enables HEINEKEN to globally support applications that generate insights with local business impact.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
