Machine learning helps major airline predict disruptions before they reach the runway

A major U.S. low-cost airline operating one of the largest domestic flight networks.

The airline had a solid monitoring foundation, but maintaining reliability across mission-critical systems demands continuous improvement. As their operations increasingly relied on cloud infrastructure built on AWS, the client needed a smarter way to detect subtle patterns earlier—before they escalated into costly domino effects across operations and passenger experience.

Nearshore anomaly detection services with machine learning, supporting mission-critical, cloud-based airline IT operations. Delivered end-to-end using AWS-native services for model training, deployment, and integration with existing observability platforms.

Built within existing resources and deployed with no added cost or client-side effort. Integrated into monitoring tools and ITSM processes and developed iteratively using AWS-native services for scalability and operational fit.

1. Developed and validated predictive anomaly detection models.
2. Automated alerting to reduce Mean Time to Detect (MTTD).
3. Built feedback loops to refine ML models and detection thresholds.
4. Delivered dashboards for operational and executive visibility.
5. AWS services included SageMaker, Fargate, Lambda, S3, Batch, and Application Load Balancer for cloud-native delivery.