The Taub Faculty of Computer Science Events and Talks

Machine learning model training is a computationally expensive task that requires significant amounts of time and resources, especially for larger models. The problem is further increased when data arrives in a continuous stream since the model must be retrained multiple times to incorporate the new data and ensure the model remains accurate. Another difficulty arises during inference time when the data is geo-distributed; centralizing all data updates can be costly and lead to network overhead. To address these challenges, we propose various optimization and monitoring algorithms that integrate first and second-order information of the function to reduce optimization time and network overhead. Our proposed solutions aim to improve the efficiency and scalability of machine learning models, ultimately reducing the time and cost required for training and monitoring.