In embodiments, methods and apparatus are disclosed for predicting bandwidth utilization for a customer of a connectivity service provider. A model that predicts bandwidth utilization is trained in a distributed manner at the network interface devices which connect customer networks to a connectivity service provider network, rather than in a centralized manner at a data center within the service provider network. The network interface devices leverage the storage of an aggregation server and the structure of bandwidth utilization trends to reduce the resources required to calculate the models. The distributed methodology allows for improved scalability in training bandwidth utilization models for all of the customers of the connectivity service provider. Relying on the periodicity of the bandwidth utilization, the method further includes predicting, using the trained model, future bandwidth utilization over time, and the identification and flagging of potential network faults when bandwidth utilization fails to meet expectations.