MLOps approach for system performance optimization for machine learning systems

There are numerous practical challenges related to development or operation of machine learning systems in real-world scenarios, and the field of MLOps brings DevOps practices from software engineering to machine learning. This thesis investigated whether using early stopping with system metrics leads to more efficient hyperparameter tuning when resource constraints exist. The experiments conducted measured system performance including mean step time, CPU utilization, and memory utilization on 4 datasets and 4 machine learning algorithms with varying hyperparameters such as batch size and learning rate. Findings indicate that increased mean step time and memory utilization with large batch sizes could potentially be leveraged for early stopping.