Practical Adoption Challenges of ML for Systems
Sunday, November 3, 2024 – co-located with SOSP 2024 (Austin, TX)
Workshop Overview
Using ML for improving computer systems has seen a significant amount of work both in academia and industry. With the recent advances in Generative AI models, the potential offered by AI has been growing rapidly. However, deployed uses of such techniques remain rare. While many published works in this space focus on solving the underlying learning problems, we observed that some of the biggest challenges of deploying ML for Systems in practice come from non-ML systems aspects, such as 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.
Important Dates
Paper submissions due: TBD
Notification to authors: TBD
Submission Site: TBD
Join the Discussion
E-meet and chat with others excited about PACMI: https://groups.google.com/g/pacmi
Contact us
Email: chairs@pacmi-workshop.org