Testing is a critical part of the software development cycle. As your software project grows, dealing with bugs and regressions can consume your team if you do not take a principled approach to testing. As a result, software testing methodologies are well-studied. However, machine learning models introduce a new set of complexities beyond traditional software. In particular, machine learning models depend on data in addition to code. As a result, testing methodologies for machine learning systems are less understood and less widely implemented in practice. In this talk, we argue for the importance of testing in ML, give an overview of the types of testing available to ML practitioners, and make recommendations about how you can start to incorporate more robust testing into your ML projects.
Overview and Author Bio
Testing Production Machine Learning Systems
Josh Tobin, PhD
Founder | Former Research Scientist | Stealth Startup |Open AI