In model development, edge cases are situations or inputs that occur at the extremes of the operating conditions or input domain, which might not be handled well by the model. They are unusual, rare, or borderline scenarios that can cause a system to fail or behave unpredictably if not accounted for. For example, an edge case in image recognition might be an upside-down image, or an image with extreme lighting, or in an autonomous driving system, an edge case might be encountering an animal on a highway. It’s crucial to identify and test edge cases because they often reveal robustness issues. Handling edge cases might involve adding specific rules, collecting additional training data for those scenarios, or designing the system to safely fail or ask for human intervention when it encounters something far outside normal experience.