Testing Stochastic AI Models with Hypothesis

Explore property-based testing for stochastic AI models using the Hypothesis library in this PyCon US talk. Dive into the challenges of testing advanced AI systems and learn how to generate random examples of plausible edge cases. Discover the theory behind property-based testing and see practical use cases demonstrating the implementation of the Hypothesis library. Cover topics such as example-based testing, properties like commutativity and invariant functions, metamorphic testing, and Hypothesis strategies. Learn to define custom strategies, transform data functions, debug Hypothesis strategies, and implement repeatable random testing with shrinking capabilities. Gain insights into additional components like image rotation to enhance your AI model testing approach.