ML Testing & Explainability - Full Stack Deep Learning - Spring 2021
Explore advanced concepts in machine learning testing and explainability in this comprehensive lecture from the Full Stack Deep Learning Spring 2021 series. Dive into software testing best practices, continuous integration and delivery, and various types of ML system tests including infrastructure, training, functionality, and evaluation. Learn about shadow tests, A/B tests, labeling tests, and expectation tests, as well as challenges in operationalizing ML tests. Gain insights into explainable and interpretable AI, including techniques for using interpretable model families, distilling complex models, understanding feature contributions, and analyzing training data point impacts. Critically examine the need for explainability and consider important caveats in the field of explainable and interpretable AI.