Today’s digital & connected customers of digital service providers (DSPs) expect anytime, anywhere, and any device access to the products and services offered along with the highest level of service assurance. Service outages can have a serious impact on brand reputation and any such incident could easily cost DSPs millions of dollars. When it comes to customer experience, reacting to a network event after it has occurred is no more acceptable.
In this challenging situation, artificial intelligence/machine learning techniques are rising to be the key technological enabler for DSPs to innovate in the service assurance domain. With this technological shift, DSPs would be able to analyze tons of data from various sources at micro levels, derive insights and take real-time as well as preventive actions. Machine learning algorithms can be trained using historical and real-time data to predict network event failures, network outages, and traffic congestion issues even before they occur.
This insight explores various AI/ML classification techniques that can be used to build an effective network event prediction model. It further discusses various factors to be considered, while improving the prediction accuracy levels.
Vijay Anand R