构建可落地的机器学习系统质量成熟度框架，助力企业提升ML可靠性与可复现性。
Maturity Framework for Enhancing Machine Learning Quality
- 提出基于实践的ML质量评估方法，涵盖从数据到部署全链路
- 通过Booking.com案例验证，系统性提升质量并带来业务收益
- 开源框架适配各类组织，推动行业标准升级
随着机器学习在商业应用中的快速渗透，确保其质量、可靠性和可复现性至关重要。本文提出一种系统化的机器学习质量评估方法，并引入面向治理的结构化成熟度框架。强调了质量的重要性及现有框架在治理方面的不足。主要贡献是经过实证验证的开放源代码质量评估方法，以及针对机器学习系统的系统性成熟度框架。基于Booking.com的大规模实践，讨论了组织内采纳过程中遇到的挑战与经验教训。研究呈现了实证发现，展示了质量改进趋势和业务成果。该成熟度框架旨在成为重塑行业标准的宝贵资源，为任何组织提供结构化路径以提升机器学习成熟度。
原文摘要 · Abstract (English)
With the rapid integration of Machine Learning (ML) in business applications and processes, it is crucial to ensure the quality, reliability and reproducibility of such systems. We suggest a methodical approach towards ML system quality assessment and introduce a structured Maturity framework for governance of ML. We emphasize the importance of quality in ML and the need for rigorous assessment, driven by issues in ML governance and gaps in existing frameworks. Our primary contribution is a comprehensive open-sourced quality assessment method, validated with empirical evidence, accompanied by a systematic maturity framework tailored to ML systems. Drawing from applied experience at Booking.com, we discuss challenges and lessons learned during large-scale adoption within organizations. The study presents empirical findings, highlighting quality improvement trends and showcasing business outcomes. The maturity framework for ML systems, aims to become a valuable resource to reshape industry standards and enable a structural approach to improve ML maturity in any organization.
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