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A consistent, robust MLOps lifecycle for insights into travel behaviour

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The client,

GVB stands for Hospitable, Connecting and Reliable. These are the core values that guide its employees as they provide public transport in Amsterdam and the surrounding area. GVB makes sure Amsterdam is accessible to everyone. With around 5,000 employees, temporary workers and external staff, it is one of Amsterdam’s largest employers – and among the city’s five best employers.

Client GVB
Industry Mobility & logistics
Services AI Solutions
Partner The Cirqle
Tools & techniques Databricks, Azure DevOps

Our solution,

GVB’s segmentation model uses travel behaviour to provide more insight into how passengers move around. This helps GVB offer suitable travel products, for example. The model needs to keep up with changes over the years. It also needs to be implemented robustly to meet GVB’s data architecture standards. 

We set up a consistent, robust MLOps lifecycle so the segmentation model can be deployed to production and maintained effectively. We also created an MLOps workflow in Databricks with several reusable templates.

It was very interesting to improve the Databricks workflow for machine learning projects by putting a robust MLOps lifecycle in place. This has laid a solid foundation for setting up future projects in a similar way.
Koen Koopen Senior Data Scientist - Data Science Lab

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