In Episode 33 of '100 Days of Data,' Jonas and Amy dive deep into the critical concepts of overfitting and underfitting in AI models. Using relatable analogies and real-world examples—from predictive maintenance to customer churn—they explain how models can be too simplistic or overly complex, both of which hurt performance. The discussion introduces key concepts like generalization, bias, and variance, emphasizing that the goal is to find the right balance for your model to perform well on new, unseen data. They explore practical approaches such as regularization, cross-validation, and appropriate model selection to manage complexity. Whether you're a data scientist or business leader, this episode offers valuable insights on how to spot and fix model performance issues before they impact outcomes.