  This work examines the challenges and opportunities of Machine Learning (ML)
for Monitoring and Operational Data Analytics (MODA) in the context of
Quantitative Codesign of Supercomputers (QCS). MODA is employed to gain
insights into the behavior of current High Performance Computing (HPC) systems
to improve system efficiency, performance, and reliability (e.g. through
optimizing cooling infrastructure, job scheduling, and application parameter
tuning). In this work, we take the position that QCS in general, and MODA in
particular, require close exchange with the ML community to realize the full
potential of data-driven analysis for the benefit of existing and future HPC
systems. This exchange will facilitate identifying the appropriate ML methods
to gain insights into current HPC systems and to go beyond expert-based
knowledge and rules of thumb.
