Drift Analysis

Drift analysis focuses on quantifying and understanding changes in data distributions over time, impacting the performance of machine learning models and algorithms. Current research emphasizes developing robust methods for detecting and mitigating drift, employing techniques like statistical process control, kernel-based tests (e.g., using Maximum Mean Discrepancy), and dynamic clustering for various data types including images and text. These advancements are crucial for ensuring the reliability and longevity of AI systems across diverse applications, from healthcare to customer service, by enabling proactive model adaptation and improved performance monitoring.