  In this community review report, we discuss applications and techniques for
fast machine learning (ML) in science -- the concept of integrating power ML
methods into the real-time experimental data processing loop to accelerate
scientific discovery. The material for the report builds on two workshops held
by the Fast ML for Science community and covers three main areas: applications
for fast ML across a number of scientific domains; techniques for training and
implementing performant and resource-efficient ML algorithms; and computing
architectures, platforms, and technologies for deploying these algorithms. We
also present overlapping challenges across the multiple scientific domains
where common solutions can be found. This community report is intended to give
plenty of examples and inspiration for scientific discovery through integrated
and accelerated ML solutions. This is followed by a high-level overview and
organization of technical advances, including an abundance of pointers to
source material, which can enable these breakthroughs.
