Browsing by Author "Ratilainen, Katja-Mari"

Context: The Bank of Finland, as the national monetary and central bank of Finland, possesses an extensive repository of data that fulfills both the statistical needs of international organizations and the federal requirements. Data scientists within the bank are increasingly interested in investing in machine learning (ML) capabilities to develop predictive models. MLOps offers a set of practices that ensure the reliable and efficient maintenance and deployment of ML models. Objective: In this thesis, we focus on addressing how to implement an ML pipeline within an existing environment. The case study is explorative in nature, with the primary objective of gaining deeper insight into MLOps tools and their practical implementation within the organization. Method: We apply the design science research methodology to divide design and development into six tasks: problem identification, objective definition, design and development, demonstration, evaluation, and communication. Results: We select the tools for the MLOps based on the user requirements and the existing environment, and then we design and develop a simplified end-to-end ML pipeline utilizing the chosen tools. Lastly, we conduct an evaluation to measure the alignment between the selected tools and the initial user requirements.