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About this paper
Angelantonio Castelli, Georgios Christos Chouliaras, Dmitri Goldenberg
Booking.com researchers developed a structured maturity framework for assessing and enhancing Machine Learning (ML) system quality, implemented across hundreds of production ML systems. This framework led to consistent increases in ML quality scores and significantly improved various quality attributes, directly impacting business value and operational efficiency.
Key insights
- Implementing a large-scale ML quality framework requires strong leadership support, broad community buy-in, and a cultural shift towards viewing assessments as "improvement opportunities" rather than judgments.
- Automated and centralized tooling is critical for scaling quality assessments efficiently across numerous ML systems, enabling systematic progress tracking and reproducibility.
- Tailoring quality requirements based on an ML system's business criticality ensures that quality improvement efforts are prioritized and proportional to its impact, avoiding unnecessary overhead.
- Articulating the direct business value of quality improvements (e.g., cost savings, reduced technical debt, sustained performance) is essential to secure organizational buy-in and investment.
Summary by alphaXiv.
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