Our paper “Heuristic-based Resource Allocation for Cloud-native Machine Learning Workloads,” has been accepted by the 16th IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). The paper presents a heuristic-based approach to distributed model training, and analyzes resource utilization for a machine learning pipeline deployed on the KubeFlow MLOps framework.
