# ML Test Score

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ML Test Score - Testing and Deployment
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## Summary

* [ML Test Score :  A Rubric for Production Readiness and Technical Debt Reduction](https://static.googleusercontent.com/media/research.google.com/en/pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf)  is an exhaustive framework/checklist from practitioners at Google.
* The paper presents a rubric as a set of 28 actionable tests and offers a scoring system to measure how ready for production a given machine learning system is. These are categorized into 4 sections: (1) data tests, (2) model tests, (3) ML infrastructure tests, and (4) monitoring tests.
* The scoring system provides a vector for incentivizing ML system developers to achieve stable levels of reliability by providing a clear indicator of readiness and clear guidelines for how to improve.