Production-Ready MLOps for Telecommunications
summary
Neural network pipeline predicts VoIP call quality at 88% prediction accuracy — a 23‑point gain over prior statistical models — and sustains 96% anomaly detection accuracy in production with 99.99% availability and sub‑50 ms edge responses. Pipeline uses Kafka for 10k events/sec ingestion, Apache Beam extracting 45+ features, Feast feature store, CubeFlow distributed GPU training, MLflow registry, TensorFlow Serving on Kubernetes with blue‑green deploy and autoscaling. Fault detection drops from 15–30 minutes to 2–5 minutes after phased rollout and automated retraining.
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chapters (7)
- 0:00Introduction and Session Overview
- 0:45Challenges with Traditional Telecom Monitoring
- 1:55ML Ops Architecture Overview
- 3:22Neural Network Model and Feature Engineering
- 5:09Deployment Strategy and Monitoring
- 6:51Business Results and Roadmap
- 7:54Conclusion and Key Takeaways
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