# ML Test Score {% embed url="" %} ML Test Score - Testing and Deployment {% endembed %} ## 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/ml-test-score.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.