  The advent of Fifth Generation (5G) and beyond 5G networks (5G+) has
revolutionized the way network operators consider the management and
orchestration of their networks. With an increased focus on intelligence and
automation through core network functions such as the NWDAF, service providers
are tasked with integrating machine learning models and artificial intelligence
systems into their existing network operation practices. Due to the dynamic
nature of next-generation networks and their supported use cases and
applications, model drift is a serious concern, which can deteriorate the
performance of intelligent models deployed throughout the network. The work
presented in this paper introduces a model drift detection and adaptation
module for 5G core networks. Using a functional prototype of a 5G core network,
a drift in user behaviour is emulated, and the proposed framework is deployed
and tested. The results of this work demonstrate the ability of the drift
detection module to accurately characterize a drifted concept as well as the
ability of the drift adaptation module to begin the necessary remediation
efforts to restore system performance.
