Building a generalisable ML pipeline at ING

Building a generalisable ML pipeline at ING

Bauman, Niels (TU Delft Electrical Engineering, Mathematics and Computer Science)

Cruz, Luis (mentor)
van Deursen, A. (graduation committee)
Yang, J. (graduation committee)

Delft University of Technology

Computer Science | Software Technology

2022-08-08

Advances in data science have caused an increase in the use of Artificial Intelligence (AI), specifically Machine Learning (ML), throughout various fields. Not only in research but in the industry as well, has ML been receiving increasing amounts of interest. Many companies rely on ML models to increase the efficiency of existing processes or offer new services and products. The industry, however, is facing several additional challenges compared to the academic context. One of those challenges is applying the Development Operations (DevOps) model to an ML application, also referred to as MLOps. This thesis sets out to find the specific challenges that practitioners encounter while operationalising ML models. To do so, we perform a single-case case study on an ML pipeline built by the Trade & Communication Surveillance team at the ING bank. This case study consists of conducting a set of interviews and performing a manual code inspection of the pipeline. The team faces challenges ranging from having insufficient time for operationalising each ML project individually to operating in the highlyregulated fintech context. Their pipeline is able to deploy a single ML model but it does not generalise well to other projects. We present the first version of an application that mitigates these challenges. The application is able to deploy ML models to the development environment at ING and can be operated by data scientists to reduce the effort of operationalising an ML model.

http://resolver.tudelft.nl/uuid:35c850eb-1d03-4185-a8c5-4469b2112327

Student theses

master thesis

© 2022 Niels Bauman