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arXiv · 2109.09231

Scaling Enterprise Recommender Systems for Decentralization

Maurits van der Goes
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Abstract

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.

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Maurits van der Goes. 2021-09-19. Scaling Enterprise Recommender Systems for Decentralization. https://doi.org/10.1145/3460231.3474616

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