Machine Learning for Systems

Machine Learning (ML) for Systems describes the application of machine learning techniques to problems related to computer systems. By leveraging supervised learning and reinforcement learning (RL) approaches, machine learning can replace longstanding heuristics that currently drive many of these systems. This includes a wide range of topics, including multi-objective tasks such as designing new data structures, integrated circuits, or design verification, as well as implementing control algorithms for applications such as compilers, databases, memory management, or ML frameworks. While the systems community increasingly recognizes the importance of ML in solving a variety of different systems problems, ML for Systems remains an emerging area without widely established best practices, methods and strategies for the application of state-of-the-art machine learning techniques. The goal of this workshop is to provide an interdisciplinary venue for ML and Systems experts to push this boundary and start new directions within the ML for Systems area. This year, we will encourage work in key emerging areas such as Large Language Model (LLM) training and serving.