A telecommunications company was facing challenges with its network infrastructure, resulting in incidents that impacted service stability. The lack of predictive insight into network infrastructure behavior made it difficult to prevent outages and increased operating costs due to reactive maintenance.
Understanding business issues and defining the problem to be solved is not a trivial task.
Predicting critical network failures before they occur.
Implementing an automated model that integrates predictions with existing operating systems.
Correctly modeling the operation to generate predictions.
Vinkos designed a predictive maintenance solution based on a machine learning model that analyzes critical alarms and anticipates potential network failures. To achieve this, a complete engineering and data science environment was built, including:
This solution enabled:
