Expired80% confidenceFactExact time
In 2015, Google systematically elaborated on the technical debt issues of ML systems in the paper 'Hidden Technical Debt in Machine Learning Systems'.
10
Sources
80%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Related Claims
VerifiedGoogle's 2015 paper "Hidden Technical Debt in Machine Learning Systems" revealed that the actual machine learning code in ML systems accounts for only a very small proportion.84% similarVerifiedIn the paper "Hidden Technical Debt in Machine Learning Systems," Google pointed out that in real ML systems, the actual ML code constitutes only a small fraction of the entire system, surrounded by large amounts of data collection, feature extraction, configuration management, serving infrastructure, and monitoring code.81% similarUnverifiedGoogle's 2017 paper 'Data Management Challenges in Production Machine Learning' points out that the most common technical debt in ML systems comes from data pipelines rather than the models themselves.79% similarUnverifiedGoogle's machine learning engineers have summarized from experience that actual machine learning code in a machine learning system typically accounts for only 5%, while the remaining 95% consists of data pipelines, service wrappers, and monitoring systems.68% similarUnverifiedGoogle Professional Machine Learning Engineer 认证难度公认较高，覆盖范围广，更强调机器学习的系统设计能力57% similar
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