Gerrit Scheppat successfully defended his Master Thesis

Machine Learning Operations (MLOps), the task of coordinating machine learning projects with multiple models and team members, is growing in importance and interest. Cloud computing resources are a popular option in this scenario due to many reasons like easily accessible computing resources, billing by usage time and further available services like a fully managed en- vironment. Two approaches to monitor models in an MLOps environment are compared by using two popular statistical time series forecasting mod- els and two datasets from a widely known forecasting competition. One approach is the default Amazon Web Services (AWS) model drift monitor- ing and the other is a tracking signal monitoring. The goal is to reduce economical and ecological costs generated by retraining deployed models with more recent data in a cloud environment. The tracking signal monitor- ing is shown to serve as a more generic approach which can reduce costs when a decreased model performance is accepted for lower training costs. The AWS monitoring with in-sample error metrics as monitoring threshold used as retraining trigger shows a better performance at a comparable level of retraining counts.

Gerrit Scheppat successfully defended his Master Thesis