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.

With the world in grasp of the COVID-19 pandemic, models predicting the spread of the virus can give indications to what extent a country is controlling the pandemic. Policymakers can decide to install so-called mitigation strategies to limit the spread of the virus. To aid the decision-making process, this report describes how a web application was created that is capable of visualising predictions on the future course of the virus spread in the Netherlands. Furthermore, the application allows for changing the spread rate of the virus to simulate both mitigation and exit strategies. Research has been conducted on how we can combine predictions and simulations of mitigation strategies in a single visual solution, in order to aid policymakers. Existing products were analysed in order to get a better understanding of the users’ wishes. Design goals were established which have been taken into account when designing and building the software. Furthermore, suitable languages and frameworks for the implementation were chosen. We have created a tool which both implements a prediction algorithm and visualises the outcomes of this algorithm in a web application. First, a visual design of the product was created after which an accompanying software architecture was established. This design and architecture were then implemented and tested accordingly. Most of the conducted tests were unit tests, but also user tests were performed. During the implementation phase, potential ethical consequences were considered and handled accordingly