Lilly Tech Systems
Model Robustness Testing
Learn how to evaluate and improve the resilience of AI models against adversarial inputs, distribution shifts, and real-world edge cases. Build models that perform reliably in production environments.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is model robustness? Why it matters for production AI, key failure modes, and the robustness testing lifecycle.
2. Robustness Metrics
Accuracy under perturbation, certified robustness radius, mean corruption error, and benchmark evaluation suites.
3. Perturbation Testing
Adversarial examples, FGSM, PGD attacks, semantic perturbations, and automated adversarial testing frameworks.
4. Distribution Shift
Covariate shift, concept drift, out-of-distribution detection, domain adaptation, and monitoring for data drift.
5. Stress Testing
Load testing ML endpoints, edge case generation, boundary analysis, and automated stress testing pipelines.
6. Best Practices
Building a robustness testing culture, CI/CD integration, reporting frameworks, and continuous monitoring strategies.
What You'll Learn
By the end of this course, you'll be able to:
Evaluate Model Resilience
Systematically test AI models against adversarial attacks, noisy inputs, and distribution shifts.
Measure Robustness
Apply quantitative metrics and benchmark suites to assess model robustness objectively.
Detect Distribution Shift
Identify when production data diverges from training data and take corrective action.
Build Robust Pipelines
Integrate robustness testing into ML workflows for continuous model validation.