Dominique Ronde

Join fellow leaders and technologists across Aviation and Transport for an unscripted, high-stakes deep dive into the future of real-time operations. While legacy batch windows and decades-old messaging formats fail to keep up with disruption, forward-thinking carriers are turning live flight updates, turnaround milestones, and crew legality signals into an immediate operational edge. Discover how to eliminate information friction across airport stands, master irregular operations (IROPS), and lay the real-time data foundation required for autonomous, agentic AI in mission-critical environments. We kindly ask that those only from related industries attend due to session capacity.

A machine learning model in production is like a ship sailing blind, everything looks fine until it slams into a reef. And by then, it's too late. This phenomenon, known as concept and model drift, is especially dangerous in real-time systems where decisions happen in milliseconds and rollback is usually not an option. If not detected early, drift doesn’t just break your models — it misprices loans, misses fraud, and even risks lives. This talk distills cutting-edge research and real production lessons into practical tools that can be apply today, even if the models are already in the wild. Based on ongoing PhD research and real-world implementations, we’ll walk through the following real live questions: - How drift manifests in event-driven ML systems — and why traditional batch monitoring fails. - Common algorithms for drift detection (i.e. DDM, EDDM, ADWIN, Page-Hinkley) — and how to benchmark them in streaming environments. - An architecture for integrating drift-aware intelligence into Flink pipelines, with hooks for alerting, model retraining, or failover strategies. - Lessons from production use cases, including trade-offs in detection latency, false positives, and system…