Cloud-Native Scalability for AI Workloads in Telecom

Overview

A leading telecom operator in Southeast Asia, serving over 100 million subscribers, was facing mounting pressure to modernize its infrastructure. With growing demand for real-time analytics, intelligent network management, and AI-powered customer experience, the operator needed to scale its AI capabilities—fast.

Client Background and Challenges

The telecom operator had begun deploying AI for use cases such as predictive maintenance, fraud detection, and personalized customer engagement. But the infrastructure was struggling to keep up.

Limited scalability for AI model training and inference.

High latency in real-time decision-making across network operations.

Fragmented data silos across business units and geographies.

Manual deployment pipelines slowing down innovation cycles.
Compliance risks due to inconsistent governance across environments.

Solution Highlights

Hybrid Cloud Architecture: Migrated AI workloads to a hybrid cloud setup with GPU-enabled clusters for scalable model training and real-time inference.

Containerized Microservices: Deployed AI services using Kubernetes, enabling modular scaling and fault tolerance.

Unified Data Fabric: Integrated structured and unstructured data into a centralized data lake, optimized for AI analytics and model training.

ML DevOps Enablement: Established CI/CD pipelines for machine learning, reducing deployment time and improving model lifecycle management.

Automated Governance: Embedded policy-as-code frameworks to ensure continuous compliance with telecom regulations and data privacy laws.

Business Impact

Model Deployment Time: Reduced from 4 weeks to under 72 hours.

Latency Reduction: Real-time AI decision latency dropped by 65%.

Infrastructure Utilization: Improved by 50% through elastic scaling.

AI Use Case Expansion: Enabled 8 new AI applications within 6 months, including churn prediction, dynamic pricing, and network anomaly detection.

Compliance Confidence: Achieved automated audit readiness with real-time dashboards and policy enforcement.

Conclusion

DeltaDot AI continues to support the client with:

Edge AI Deployment for real-time analytics at cell towers and remote sites.

Federated Learning to enable secure collaboration across regional data centers.

AI Governance Automation to ensure ethical and auditable AI operations.

This case study demonstrates how cloud-native infrastructure is the foundation for scalable, secure, and impactful AI in telecom.

Case Study