{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Concept Drift — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Concept Drift</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A phenomenon where the statistical relationship between input data and the target variable changes over time, causing a deployed model's performance to degrade. For example, a fraud detection model trained on pre-pandemic spending patterns may become less accurate as consumer behavior shifts. Detecting and responding to concept drift is a core part of maintaining AI systems in production.  See also: Model Monitoring, batch learning, online learning.</p><a href=\"https://gaks.ai/glossary/concept-drift\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/concept-drift →</a></div>"}