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 observed from an industry vantage point 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.
The goal of this workshop is to raise awareness of these problems and bring together practitioners (both on the production systems and ML side) and academic researchers, to work towards a methodology of 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.
The workshop will uniquely facilitate this conversation by providing a venue for lightweight sharing of anecdotes and experiences from real-world deployments, as well as giving researchers a venue for sharing early-stage work on addressing these problems.