Scaling Enterprise Recommender Systems for Decentralization

TL;DR

This paper presents a scalable machine learning operations approach with five best practices to support decentralized recommender systems in large organizations, exemplified by HEINEKEN's deployment to enhance local business insights.

Contribution

It introduces a practical framework combining pipeline automation, data management, and security policies to enable scalable, low-debt deployment of recommender systems across decentralized units.

Findings

Faster deployment to subsidiaries

Reduced technical debt

Enhanced local business insights

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.