Data Shift and Model Adaptation in Machine Learning

Explore the principles of data shift and model adaptation in machine learning through this comprehensive 2-hour tutorial presented by experts from the Toronto Machine Learning Series. Delve into the challenges faced by machine learning models in dynamic environments where data distributions change over time. Learn strategies for detecting dataset shift, adaptation techniques, and advanced topics in data shift. Gain hands-on practice and insights from industry professionals, including Sedef Akinli Kocak from Vector Institute, Ali Pesaranghader from LG Toronto AI Lab, and Mehdi Ataei from Autodesk. Discover how to address predictive problems arising from dataset shift, particularly in unexpected scenarios like the COVID-19 pandemic or cyber attacks. Enhance your understanding of machine learning model deployment and adaptation in real-world situations.