Predicting network faults with ultimate precision using AI

Service providers ditch rule-based firefighting and embrace proactive AI to anticipate and prevent outages, saving millions and boosting customer satisfaction.

Network Operations Centers (NOCs) are pivotal for service providers in ensuring seamless connectivity and optimal performance. However, a surge in 5G, IoT, and virtualization technologies has brought unprecedented challenges. NOCs grapple with the overwhelming influx of alarms, struggling to differentiate critical issues from irrelevant ones. Manual reduction methods and rule-based approaches lead to delays and false alarms, inflating costs and hindering response efficiency.

To overcome these challenges, service providers must embrace a proactive approach, harnessing machine learning (ML) for precise prediction and resolution of network faults. Service providers can leverage ML to analyze extensive and diverse data sets, extract crucial insights and promptly implement preventative measures in real time.

Hence, adopting a network event prediction model becomes imperative to anticipate and proactively mitigate potential network failures and outages. It also enables service providers to ensure precise predictions, reduce network downtime, and cut operational expenses.

Fig: Key steps to leverage ML model for ticket prediction
and prioritization