In this presentation, Thor Larsen, Data Scientist at Noitso, dives into his experience of implementing MLOps and Explainability and how his team uses Seldon to help provide their customers with quick and accurate credit ratings, scorecards and risk profiles. Hear first-hand the challenges and solutions to effective model deployment and moving time-to-value of models from days to hours.

Check out our recent case study with Noitso on how they used Seldon to power their machine learning operations.

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.