Floris Hoogenboom is the Lead Data Scientist at Royal Schiphol Group and oversees all machine learning & AI development that happens at the Airport in house. Together with four teams of data scientists, engineers and researchers he focuses on making Schiphol a better, more efficient and more sustainable airport through the use of data.
Before joining Schiphol Floris worked as a Data Science consultant and before that he worked in one of Netherland’s biggest banks. He has a background in mathematical statistics.
May 27, 2021 11:00 AM PT
At Schiphol airport we run a lot of mission critical machine learning models in production, ranging from models that predict passenger flow to computer vision models that analyze what is happening around the aircraft. Especially now in times of Covid it is paramount for us to be able to quickly iterate on these models by implementing new features, retraining them to match the new dynamics and above all to monitor them actively to see if they still fit the current state of affairs.
To achieve those needs we rely on MLFlow but have also integrated that with many of our other systems. So have we written Airflow operators for MLFlow to ease the retraining of our models, have we integrated MLFlow deeply with our CI pipelines and have we integrated it with our model monitoring tooling.
In this talk we will take you through the way we rely on MLFlow and how that enables us to release (sometimes) multiple versions of a model per week in a controlled fashion. With this set-up we are achieving the same benefits and speed as you have with a traditional software CI pipeline.