Reducing Flight Delays with Kubernetes and Tensorflow

Schiphol Group is a group of airports, best known for Amsterdam Schiphol Airport. Schiphol is the third busiest airport in Europe. Due to its location, Schiphol is unable to build substantial new infrastructure to increase capacity. In an effort to increase on-time performance, a broad initiative was started to gain insight into the cause of delays. One of these possible causes is the 'turnaround' process. During a turnaround, an arriving aircraft is 'turned-around' to become a departing one. This process includes events such as re-fuelling. The turnaround is fully arranged and coordinated by the airline - resulting in a variety of handlers and differences in (order of) procedures. Schiphol is not involved in organising this, and therefore doesn't have detailed information about the events. Most importantly, whether the aircraft will be able to leave on time. In this talk, we'll discuss how Schiphol approached this problem with an innovative Deep Learning initiative. Our solution generates events as they happen by analyzing a real-time feed of camera images of the aircraft at the gate. We'll focus on how we set up the streaming pipeline and the challenges we faced, such as running GPU-backed infrastructure in production. Our main components are Apache Kafka and Tensorflow, backed by Azure Kubernetes Service. We'll explain how we went from a manual, batch-based Tensorflow process to a fully-automated, near real-time streaming solution.