Join our upcoming meetup hosted by the Danish Data Science Community and Copenhagen Fintech, open for everyone in the community looking to network, learn and have a good time with others in data science as well as understand and hear use cases of machine learning within the finance and banking industry.

The event is currently on an first-come-first-served to the Danish Data Science Community, and will be open shortly after to everyone in the community. Limited seats are available and the event will be held in-person at the Copenhagen Fintech Lab Lounge.

If you have questions regarding the event, please connect with Thor (thla@noitso.dk) and Nicolette (nth@copenhagenfintech.dk).

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Preliminary Agenda

16:30 Welcome

Welcome drinks and settling in

A warm welcome to the event (Nicolette Tham, Head of Community and Events at Copenhagen Fintech)

Opening notes (Kasper Groes Albin Ludvigsen, Researcher @ Hybrid Greentech and Board member @ Danish Data Science Community)

16:45 Talks with Q&A

"Why open-source is a great way to gain competitive edge" by Martin Carsten Nielsen, Founder and CEO @ Alvenir

• The talk will address how open-source contributions have helped propelled business innovation whilst examining the impact Alvenir's open-core strategy on the development of the company's relevance in the market. I will argue that in abstentia of a dedicated R&D team and extensive compute resources open-source is the only realistic alternative to staying competitive in the AI-as-a-service space.

"Why MLOps and Explainability is crucial for credit scoring models and all other models" by Thor Larsen, Data Scientist @ Noitso and Board Member @ Danish Data Science Community

• There is plenty of data out there. In the Nordic financial sector, we are blessed with more data than most, and, in Noitso, we harness this data. However, creating and deploying machine learning models for a production setting is hard. There is also high risk, one mistake in prod will impact your bottom line when dealing out credit. Organisations need MLOps. This encompasses many important things; among others reproducibility, rolling-deployments and online monitoring of data drift and outliers. On top of all this, you also need compliant explanations of what your model is predicting. In the real world, this is true for all predictions based on machine learning.

18:00 Onwards

Happy hour and networking with the communities (Danish Data Science and Copenhagen Fintech)