ML in mobility
How can Machine Learning contribute to innovations in the mobility sector? At our Machine Learning in Mobility (MLMOB) event last week, organised together with Moqod, we zoomed in on this topic.
MLMOB was our first event in a series of “Machine Learning in …” events. The goal of these events is to give insights in the opportunities and challenges of machine learning in different fields. Where we feel that some AI or ML events are far fetched and give you no clue at all on how to start, we hope to realise the opposite with these events. Real use cases are presented, telling you why companies start with ML, what incredible results you can achieve, but also which struggles you might have.
At MLMOB four very engaging sessions contributed to this story, each from a different perspective.
We started with Tom van Arman from Tapp who showed us the opportunities of ML for the mobility sector as a whole.
Next on stage were Yource (Vlucht-vertraagd) and SnappCar, both sharing their complete ML journey. These sessions gave the insight that not technical, but organisational issues like model acceptance might prove to be the biggest challenge. They also showed the immense impact ML can have on business. For Yource, implementing machine learning resulted in an automation of 70% of their claims assessment, which used to take over 5 minutes per claim! For SnappCar it meant a 14% increase in conversion.
As a more technical deep dive we concluded the day with Taras Slipets from Flixbus. In this session we discovered how to create scalable ML infastructures.
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