Network Performance Optimization Using Real-Time ML Pipelines
ConnectX Telecom served 15M+ subscribers but struggled with network congestion during peak hours. Kansoft built a real-time ML pipeline on AWS that analyzes network traffic patterns, predicts congestion 30 minutes ahead, and auto-scales resources proactively.
The system processes terabytes of network telemetry data in real-time.
Results Achieved
- 52% reduction in network downtime through predictive scaling
- 30-minute congestion prediction window with 94% accuracy
- Automated resource allocation across 2,400 cell towers
- Customer churn reduced by 18% in the first quarter post-deployment
- Network capacity utilization improved from 62% to 85%
Client Overview
ConnectX Telecom served 15M+ subscribers but struggled with network congestion during peak hours. Their reactive monitoring approach meant issues were detected only after users experienced degraded service, leading to rising churn rates.
- Industry Telecom
- Technology Stack Apache Flink, PyTorch, Kubernetes, AWS SageMaker
- Services Used Data & AI, Cloud & DevOps
- Project Duration 8 months
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