  In High Energy Physics facilities that provide High Performance Computing
environments provide an opportunity to efficiently perform the statistical
inference required for analysis of data from the Large Hadron Collider, but can
pose problems with orchestration and efficient scheduling. The compute
architectures at these facilities do not easily support the Python compute
model, and the configuration scheduling of batch jobs for physics often
requires expertise in multiple job scheduling services. The combination of the
pure-Python libraries pyhf and funcX reduces the common problem in HEP analyses
of performing statistical inference with binned models, that would
traditionally take multiple hours and bespoke scheduling, to an on-demand
(fitting) "function as a service" that can scalably execute across workers in
just a few minutes, offering reduced time to insight and inference. We
demonstrate execution of a scalable workflow using funcX to simultaneously fit
125 signal hypotheses from a published ATLAS search for new physics using pyhf
with a wall time of under 3 minutes. We additionally show performance
comparisons for other physics analyses with openly published probability models
and argue for a blueprint of fitting as a service systems at HPC centers.
