In this talk, we will cover how to write effective test cases for machine learning (ML) libraries that are used by hundreds of thousands of users on a regular basis. Tests, despite their well-established need for trust and foolproofing, often get less prioritized. Later, this can wreak havoc on massive codebases, with a high likelihood of introducing breaking changes and other unpleasant situations. This talk deals with our approach to testing our ML libraries, which serve a wide user base. We will cover a wide variety of topics, including the mindset and the necessity of minimal-yet-sufficient testing, all the way up to sharing some practical examples of end-to-end test suites.
