How to Stress Test Your AI Models Without Collecting New Data

AI models rarely fail in controlled lab environments—they fail in the real world. In this webinar, you’ll learn how to use the Natural Robustness Toolkit (NRTK) to evaluate AI model behavior using synthetic perturbation testing. Instead of collecting new field data, discover how to apply controlled disturbances to existing datasets to simulate real-world operating conditions and identify weaknesses before deployment. In this webinar, you’ll discover how to: Install and validate the NRTK Python package. Run perturbations on sample imagery to simulate operational conditions. Configure controlled parameter sweeps to systematically stress-test model performance. Expand existing datasets with operationally relevant perturbations. Generate reproducible robustness evaluations for AI systems. Why watch: Improve how you validate AI models in operational contexts. Learn a practical workflow for robustness testing without collecting new field data. See how synthetic perturbation testing can reveal model weaknesses before deployment. Hear directly from the NRTK development team about the recent v1.0 release. Ask implementation questions during a live Q&A with the developers. Resources & Links: Explore Natural Robustness Toolkit (NRTK): https://nrtk.readthedocs.io/en/stable... Contact our team for personalized guidance or project inquiries: https://www.kitware.com/contact/ Subscribe for more tutorials! #NRTK #AIRobustness #AITestAndEvaluation #MachineLearning #ComputerVision #AIValidation #OpenSourceAI #ModelTesting #SyntheticData #AIEngineering