Service providers are spending billions of dollars a year dealing with network outages, service degradation and growing security threats. The increase in network traffic is a key factor resulting in these challenges. Consequently, automation is considered critical to enabling service providers to address operations and management issues resulting from this traffic increase. The traditional rules-based approach to implementing automation will not be sufficient to support the level of activity needed to control, manage and secure the network. In order to automate operations, additional variables and metrics will need to be assessed to proactively detect when events of interest occur, analyze them, identify and execute appropriate responses.

To address these challenges, service providers are turning to advanced analytics techniques that are flexible, adaptive, and intelligent. In this webinar we will discuss how machine learning (ML)-based analytics, as an advanced analytics function, can enhance service providers' automation objectives with capabilities to detect and resolve network issues, plan network growth, optimize network performance, prevent security threats, reclaim revenue leakage, and more

Key topics include:

Value proposition on the role of ML-based analytics and automation in addressing the operator's business challenges
Data modeling complexity today, and where is it going
Use cases where ML can be deployed to address network operations, fraud challenges, and to detect correlations between problem areas and underlying protocols and network functions
Highlight strategies that operators must adopt to successfully implement ML-based analytics to automate their networks

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