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)
Google Scholar

Abstract

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.