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.

中文翻译：

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扩展企业推荐系统以实现去中心化

在去中心化组织中，本地对支持业务流程的推荐系统的需求不断增长。数据源和基础设施的多样性挑战了中央工程团队。在没有技术债务的情况下实现高交付速度需要在推荐系统的开发和运营中采用可扩展的方法。在 HEINEKEN 公司，我们使用五个最佳实践执行机器学习操作方法：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、自动化和协作文化，以扩展推荐系统以实现去中心化。我们展示了自助服务 ML 工作区部署和推荐系统的实际用例，该系统可以更快地扩展到子公司并减少技术债务。