In the context of AI, particularly in machine learning and deep learning, an epoch refers to one complete pass through the entire training dataset by the learning algorithm. During an epoch, the model's internal parameters are adjusted in an attempt to learn the patterns within the data. Multiple epochs are often necessary for the model to converge to a high level of accuracy or minimize the error in its predictions. The number of epochs is a hyperparameter that is set before training begins and can greatly affect the model's performance. Too few epochs can result in underfitting, while too many can lead to overfitting.