The ML Test Score by Google

Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, and D. Sculley, The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction, Proceedings of IEEE Big Data, 2017.

Creating reliable, production-level machine learning systems brings on a host of concerns not found in small toy examples or even large offline research experiments. Testing and monitoring are key considerations for ensuring the production-readiness of an ML system, and for reducing technical debt of ML systems. But it can be difficult to formulate specific tests, given that the actual prediction behavior of any given model is difficult to specify a priori. In this paper, we present 28 specific tests and monitoring needs, drawn from experience with a wide range of production ML systems to help quantify these issues and present an easy to follow road-map to improve production readiness and pay down ML technical debt.

Hidden Technical Debt in Machine Learning Systems 的延續。分為  feature tests、model tests、ML infrastructure tests、和 production monitoring，並訪問了 36 個 Google 團隊，瞭解四個面向的執行程度。