Call for Papers

Important Dates

Paper submissions due: Friday, March 31, 2023, 11:59 PM AoE

Notification to authors: Tuesday, April 18, 2023


Using ML for improving computer systems has seen a significant amount of work both in academia and industry. However, deployed uses of such techniques remain rare. While many published works in this space focus on solving the underlying learning problems, we observe a gap between these fundamental learning methods and actually using these methods to drive real world impact. The goal of this workshop is to raise awareness of this gap and to seek understanding of why it occurs. Examples of topics include: Highlighting potential issues with ML feature stability, reliability, availability, ML integration into rollout processes, verification, safety guarantees, feedback loops introduced by learning, debuggability, and explainability. 

We hope to identify these problems and bring together practitioners and academic researchers, both on the production systems and ML side, to work towards a methodology for capturing these problems in academic research. We believe that starting this conversation between the academic and industrial research communities will facilitate the adoption of ML for Systems research in production systems, and will provide the academic community with access to new research problems that exist in real-world deployments but have seen less attention in the academic community.

To this end, we invite lightweight submissions (between 1-4 pages, excluding references and appendices) in the broad area of challenges associated with using machine learning in computer systems. We encourage a wide variety of submissions, from success-stories to retrospectives, and are particularly interested in anecdotes and experiences from real-world deployments. We also encourage academic and research submissions that are describing techniques that could be applicable to such deployments, even if they do not describe a real system.

Topics of Interest

Some areas of interest include:

We define computer systems to broadly include (not limited to): Computer Architecture, Network Systems, Operating Systems, Runtime Systems, Software Systems, Code Modeling, Compilers, Databases, Data Centers, Distributed Systems, Security and Performance Tools. We define ML to broadly include any data-driven methods (not limited to deep learning).

Submission Instructions

conferences. The workshop will also include a series of invited talks, panels, and breakout sessions.

Submission Site: