Scaling Enterprise Recommender Systems for Decentralization
This work addresses the problem of managing diverse data and infrastructure for enterprise teams in decentralized settings, though it is incremental as it applies existing MLOps practices to a specific domain.
The paper tackles the challenge of scaling recommender systems across decentralized organizations by implementing a machine learning operations method with five best practices, resulting in faster deployment to subsidiaries and reduced technical debt.
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.