Predictive zero-touch network and systems reconciliation using artificial intelligence and/or machine learning

Abstract

Aspects of the subject disclosure may include, for example, obtaining first information indicative of one or more historical operating characteristics of a network; obtaining second information indicative of one or more current operating characteristics of the network; comparing, via a first computer-implemented process that requires no manual intervention, the first information to the second information to make a prediction of a potential future network event, resulting in a predicted future network event; classifying, via a second computer-implemented process that requires no manual intervention, the predicted future network event into one of a plurality of classes of network events; and responsive to the classifying, facilitating, via a third computer-implemented process that requires no manual intervention, an action to at least partially avoid an occurrence of the predicted future network event. Other embodiments are disclosed.