Machine learning-based cellular service issue troubleshooting using limited ground truth data

Abstract

Aspects of the subject disclosure may include, for example, training a first machine learning model based on a combination of labeled training data and unlabeled training data, the first machine learning model producing augmented training data, training a second machine learning model based on a combination of the labeled training data and the augmented training data, receiving, at a client device, customer information about a service degradation at a user equipment (UE) device of a customer in a cellular network, providing the customer information to the second machine learning model, receiving, at the client device, from the second machine learning model, information identifying a root cause of the service degradation, and modifying a network component of the cellular network or the UE device, based on the information identifying a root cause of the service degradation. Other embodiments are disclosed.