Uploaded April 2026 | Updated September 2026, 3 weeks ago
#VDZ26 Moving a machine learning model from exploration into production is never a straight path.
Academic success often relies on clean datasets, controlled environments, and benchmark metrics—but in the real world, models face data drift, latency constraints, integration challenges, and the risk of wrong predictions directly impacting customers and business processes.
In this talk, we share our journey of bringing fraud detection models from research into production at Swiss Post. We highlight the role of the shadow model approach, where new models run in parallel with production systems to safely validate performance on live traffic.
You’ll learn how shadow testing helps measure robustness, monitor data drift, and align with business KPIs—without introducing operational risk.
Attendees will take away concrete practices for bridging the gap between academic experimentation and real-world operations: how to design safe rollouts, build monitoring pipelines, and decide when a model is ready for prime time.
#VDZ26 Moving a machine learning model from exploration into production is never a straight path.
Academic success often relies on clean datasets, controlled environments, and benchmark metrics—but in the real world, models face data drift, latency constraints, integration challenges, and the risk of wrong predictions directly impacting customers and business processes.
In this talk, we share our journey of bringing fraud detection models from research into production at Swiss Post. We highlight the role of the shadow model approach, where new models run in parallel with production systems to safely validate performance on live traffic.
You’ll learn how shadow testing helps measure robustness, monitor data drift, and align with business KPIs—without introducing operational risk.
Attendees will take away concrete practices for bridging the gap between academic experimentation and real-world operations: how to design safe rollouts, build monitoring pipelines, and decide when a model is ready for prime time.
![[VDBUH2026] Victor Botan - Scaling the Future: Lakehouse Architecture and OpenSource Collaboration](https://i.ytimg.com/vi/lW6VqE3HMvQ/mqdefault.jpg "[VDBUH2026] Victor Botan - Scaling the Future: Lakehouse Architecture and OpenSource Collaboration
Modern data platforms demand scalability, flexibility, and interoperability—capabilities that traditional architectures often fail to deliver. The lakehouse paradigm bridges this gap by combining the strengths of data lakes and data warehouses into a unified, performant system.
This talk explores how open source technologies like Apache Spark, Delta Lake, and Apache Iceberg enable scalable lakehouse architectures. It also highlights the strategic value of contributing back to these ecosystems—driving innovation, improving reliability, and shaping the future of data engineering. Attendees will leave with practical insights on building resilient data platforms and leveraging open source as a force multiplier.")
