Unverified50% confidenceFactExact time
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%
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50%
Confidence
Long-term
Relevance
8/20/2026
First Seen
Sources
Related Claims
VerifiedIn 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.77% similarVerifiedGoogle'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.70% 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.67% similarVerifiedGoogle's MLOps maturity model divides ML systems into levels 0 through 2, with Level 2 achieving a complete CI/CD/CT closed loop.67% similarUnverifiedGoogle 在其论文《Rules of ML》中建议在确认简单模型已充分优化之前不要急于引入复杂模型64% similar
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