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In 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.
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8/1/2026
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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.87% similarUnverified据Google 2023年研究报告，在实际生产ML系统中，模型代码仅占整个系统代码的5-10%77% 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.77% 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.76% similarUnverified典型的ML系统设计题目包括设计推荐系统、实时欺诈检测系统、大规模文档搜索引擎等67% similar
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