Distribution Drift

Distribution drift, the change in data distribution over time, poses a significant challenge for machine learning models, impacting their accuracy and reliability. Current research focuses on developing robust methods for detecting and adapting to this drift, employing techniques such as data sketching, state-space models, and variational inference within various model architectures including Graph Neural Networks, Variational Autoencoders, and CLIP models. These advancements are crucial for maintaining the performance of deployed models across diverse applications, from medical imaging diagnostics to performance modeling in high-performance computing and API usage monitoring, ensuring continued accuracy and reliability in dynamic environments.