A Scalable and High Availability Solution for Recommending Resolutions to Problem Tickets

TL;DR

This paper presents a scalable, high-availability machine learning system that leverages clustering, NLP, and advanced models to improve incident resolution recommendations in telecom and service industries.

Contribution

It introduces a robust ML-driven framework combining clustering, supervised learning, and NLP models, along with a real-time dashboard and Kubernetes deployment for incident resolution.

Findings

High prediction accuracy on open-source and proprietary datasets

Effective handling of data drift and missing data issues

Scalable deployment with high availability

Abstract

Resolution of incidents or problem tickets is a common theme in service industries in any sector, including billing and charging systems in telecom domain. Machine learning can help to identify patterns and suggest resolutions for the problem tickets, based on patterns in the historical data of the tickets. However, this process may be complicated due to a variety of phenomena such as data drift and issues such as missing data, lack of data pertaining to resolutions of past incidents, too many similar sounding resolutions due to free text and similar sounding text. This paper proposes a robust ML-driven solution employing clustering, supervised learning, and advanced NLP models to tackle these challenges effectively. Building on previous work, we demonstrate clustering-based resolution identification, supervised classification with LDA, Siamese networks, and One-shot learning, Index embedding. Additionally, we present a real-time dashboard and a highly available Kubernetes-based production deployment. Our experiments with both the open-source Bitext customer-support dataset and proprietary telecom datasets demonstrate high prediction accuracy.