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.
Building on the success of the first iteration of PACMI at MLSys ‘22, 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, and giving researchers a
venue for sharing early-stage work on addressing these problems.