Lecture 10: ML Testing Explainability ( Full Stack Deep Learning Spring 2021)

In this lecture, you will expose to concepts and methods to help you, your teams, and your users: (1) understand at a deeper level how well your model is performing, (2) become more confident in your model s ability to perform well in production, (3) understand the model s performance envelope. 00:00 What s Wrong With BlackBox Predictions 06:47 Types of Software Tests 08:05 Software Testing Best Practices 21:02 Sofware Testing In Production 26:42 Continuous Integration and Continuous Delivery 29:25 Testing Machine Learning Systems 36:39 Infrastructure Tests 38:13 Training Tests 41:24 Functionality Tests 42:51 Evaluation Tests 01:01:27 Shadow Tests 01:03:58 A, B Tests 01:05:40 Labeling Tests 01:07:36 Expectation Tests 01:11:43 Challenges and Solutions Operationalizing ML Tests 01:17:29 Overview of Explainable and Interpretable AI 01:20:00 Use An Interpretable Family of Models 01:23:49 Distill A Complex To An Interpretable One 01:27:52 Understand The Contribution of Featu