Abstract: This disclosure is directed to automated computer-implemented methods and systems for detecting and correcting a trending problem with an application executing in a data center. The methods receive a new support request entered via a graphical user interface. The methods perform trend discovery of the new support request over recent time windows using a pre-trained and fine-tuned model bidirectional encoder representation from transformer. In response to detecting a trending problem described in the new support request, the method discovers recommended remedial measures for the new support request based on similar support requests previously recorded in a support request data store or on similar knowledge base articles previously recorded in a knowledge base data store. The recommended remedial measures for correcting the trending problem are executed using an operations manager of the data center.

Abstract: An AI-driven support system is described herein. This system includes a request formed from least one of a support request and a knowledge base. The system also includes an extractor module made up of a data pipeline configured to construct a training dataset from an input of at least one of said support request and said knowledge base, a training pipeline configured to take said training dataset use a BERT language model to generate at least one feature vector, and an evaluation pipeline fit to compare outputs from at least one iteration of said training pipeline, as well as output at least one parsed feature vector.