Distribution Shift
Distribution shift is a machine learning concept where the statistical distribution of data changes between training and deployment environments, leading to degraded model performance. It occurs when the real-world data a model encounters differs from the data it was trained on, such as changes in user behavior, sensor drift, or domain differences. This phenomenon is critical in production ML systems as it can cause models to make inaccurate predictions or fail unexpectedly.
Developers should learn about distribution shift when building and deploying machine learning models in dynamic real-world applications, such as fraud detection, autonomous vehicles, or recommendation systems, where data evolves over time. Understanding this concept helps in designing robust models, implementing monitoring systems to detect performance degradation, and applying techniques like domain adaptation or continual learning to maintain accuracy. It is essential for ensuring model reliability and avoiding costly failures in production environments.