Automated discovery of network inventory from raw configuration files using machine learning

Abstract

An automated network inventory discovery method, including: at a network inventory discovery engine coupled to a network data source, receiving unstructured network configuration data associated with a network element (NE); and, using a trained machine learning (ML) model, parsing named entity attributes from text of the unstructured network configuration data and mapping the named entity attributes to a common information model having a predetermined data structure. The trained ML model includes a trained named entity recognition (NER) model and/or a trained large language model (LLM). Alternatively, the trained ML model includes a trained NER model that is used to pre-process the text of the unstructured network configuration data prior to feeding resulting data into a trained LLM. The trained ML model serves the function of a custom parser script that is specific to one or more of the unstructured network configuration data or the network data source.