Disaggregated serving refers to a software architecture approach where different components of a machine learning model or application are separated and served independently, allowing for greater flexibility, scalability, and efficiency. This technique is gaining traction in the tech community as it enables developers to optimize and update individual model components without affecting the entire system, leading to improved performance, reduced latency, and enhanced overall user experience.