  The promise of multi-messenger astronomy relies on the rapid detection of
gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to
maximize the amount of time available for follow-up observations. In recent
years, neural-networks have demonstrated robust non-linear modeling
capabilities and millisecond-scale inference at a comparatively small
computational footprint, making them an attractive family of algorithms in this
context. However, integration of these algorithms into the gravitational-wave
astrophysics research ecosystem has proven non-trivial. Here, we present the
first fully machine learning-based pipeline for the detection of gravitational
waves from compact binary coalescences (CBCs) running in low-latency. We
demonstrate this pipeline to have a fraction of the latency of traditional
matched filtering search pipelines while achieving state-of-the-art sensitivity
to higher-mass stellar binary black holes.
