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.
During this workshop, we will have invited talks, panels and presentations with the aim of identifying these problems and bringing 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. For more details about submissions, please refer to the Call for Papers.