Automated Testing of AI-ML Models [Testμ 2024]

In this session, Toni Ramchandani discussed the critical role of testing in developing AI/ML models as these technologies reshape industries. He covered essential testing techniques for validating AI/ML models and highlighted functional testing, regression testing, performance testing, security testing, and user interface testing as key areas where automated tests were designed to verify that software functions as expected. Toni also discussed the prerequisites for AI/ML testing, including high-quality data, understanding of algorithms and model behavior, clear testing objectives, a testing framework, and performance benchmarks. He highlighted several tools essential for testing AI models, such as DeepExplore, SHAP, CleverHans, and Foolbox, which address different facets of AI model validation. Toni emphasized the importance of addressing both security concerns and ethical implications when developing and deploying AI models and suggested a multi-layered strategy to mitigate AI hallucinations. Finally, he discussed the future of AI testing, including continuous testing, AI-driven testing, sophisticated testing methodologies, collaboration and open-source contributions, and adapting to new technologies.