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What’s your ML test score? A rubric for ML production systems

Eric Nielsen
Michael Salib
Reliable Machine Learning in the Wild - NIPS 2016 Workshop (2016)


Using machine learning in real-world production systems is complicated by a host of issues not found in small toy examples or even large offline research experiments. Testing and monitoring are key considerations for assessing the production-readiness of an ML system. But how much testing and monitoring is enough? We present an ML Test Score rubric based on a set of actionable tests to help quantify these issues.