Machine learning promises to solve complex problems for telecommunication operators. And machine learning will be much faster, cheaper, and sustain more accuracy than the best human domain experts could ever hope to achieve. Using machine learning, from Federos, telecommunication operators gain the ability to predict, identify, and quickly isolate network faults on their network. They can detect performance degradation too, so that the network operations team can quickly restore full service. Using machine learning in telecommunications networks is possible because of the use of high-quality datasets, advances in computer processing, and by teaming telco domain experts with data scientists to develop robust machine learning algorithms.
The best way to think about machine learning is that it processes millions of data sets and searches for patterns. And it does this at a much higher rate of accuracy than traditional methods. Today’s telecommunication network operations centers process billions of events daily. Federos Machine Learning helps the operators by analyzing specific fault, performance, and event streams; enabling them to understand the context of alarms and interpret any potential service impacting events. This is important as in most cases service impacting events will violate Service Level Agreements (SLA’s). Avoiding SLA breaches has a direct and positive financial impact and also improves customer satisfaction levels. Federos machine learning can help to avoid widespread network outages borne of the network complexity and rare events. Machine learning handles complexity and rarity better and faster than the best NOC experts.It will identify, isolate, and detect the root cause fast.
The field of machine learning (ML) as applied to the telecommunication network is in its infancy. To date, most ML use cases have been applied to voice assist bots in customer care. ML applied to the NOC has the potential to improve customer satisfaction by reducing MTTR and lower operational cost by offloading outlier detection to machines not humans.
The future of machine learning and its use in telecommunication service providers will proliferate over the next several years. This will be driven by additional use cases that prove the approach to be more cost-effective while delivering an improved customer experience. The biggest barrier to the adoption of ML-based solutions is cultural beliefs and not the technology itself. CSPs considering ML technology should consider working closely with suppliers that have proven use cases and a collaborative approach: Suppliers that will share their domain expertise to avoid waste and achieve a fast implementation.
Federos is at the forefront in applying machine learning within the telecommunications industry, and this fully integrated solution delivers the pace required to Assure Business Availability at Scale.