Giovanni Degiorgi is a forward-thinking Solution Architect at Swiss Post, based in Lugano, Switzerland. With a keen focus on digital transformation, Giovanni plays a crucial role in guiding the organization's transition to cloud technologies. His expertise in designing and implementing robust cloud solutions has been instrumental in driving innovation and efficiency within Swiss Post.
Giovanni recently enhanced his technical prowess by completing a Master's degree in Machine Learning and Artificial Intelligence from SUPSI (University of Applied Sciences and Arts of Southern Switzerland). This advanced education has equipped him with cutting-edge knowledge, allowing him to bridge the gap between traditional IT infrastructure and modern data-driven technologies.
In his current role, Giovanni is actively supporting the development team at Swiss Post in establishing foundational blueprints for cloud architecture. His efforts are particularly focused on implementing MLOps (Machine Learning Operations) practices using the AWS platform. This initiative aims to streamline the deployment, monitoring, and management of machine learning models in production environments.
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.
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