Distribution Shift

Distribution shift refers to the change in the underlying data distribution that a machine learning model is trained on versus the data it encounters in real-world applications, potentially degrading its performance. As AI models are increasingly deployed in dynamic environments, understanding and addressing distribution shift is crucial for ensuring their reliability and accuracy, making it a key area of research in the machine learning community.