Streamlining Data-Centric ML-Ops in the Integrated Control System at ESS
This thesis in collaboration with ESS proposes a workflow for developing, deploying, and integrating machine learning models into the control system. Machine learning offers a promising potential for control systems but often introduces technical debt, with the actual model code representing only a small fraction of the total code base. To address this a data-centric process is proposed for retrie
