Description
Imagine a self-driving car encountering unexpected fog or a medical diagnosis system facing corrupted patient data. These scenarios highlight the critical need for robustness in AI. A robust AI model maintains its performance even when faced with noisy, unexpected, or adversarial inputs.
Here are the essential steps of robustness testing, and the techniques to evaluate and enhance the resilience of your AI models. From identifying potential vulnerabilities to implementing targeted testing strategies, here’s how to build AI systems that can confidently navigate the uncertainties of the production environments.
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