  Machine Learning (ML) methods have seen widespread adoption in seismology in
recent years. The ability of these techniques to efficiently infer the
statistical properties of large datasets often provides significant
improvements over traditional techniques. With the entire spectrum of
seismological tasks, e.g., seismic picking, source property estimation, ground
motion prediction, hypocentre determination; among others, now incorporating ML
approaches, numerous models are emerging as these techniques are further
adopted within seismology. To evaluate these algorithms, quality controlled
benchmark datasets that contain representative class distributions are vital.
In addition to this, models require implementation through a common framework
to facilitate comparison. Accessing these various benchmark datasets for
training and implementing the standardization of models is currently a
time-consuming process, hindering further advancement of ML techniques within
seismology. These development bottlenecks also affect "practitioners" seeking
to deploy the latest models on seismic data, without having to necessarily
learn entirely new ML frameworks to perform this task. We present SeisBench as
a software package to tackle these issues. SeisBench is an open-source
framework for deploying ML in seismology. SeisBench standardises access to both
models and datasets, whilst also providing a range of common processing and
data augmentation operations through the API. Through SeisBench, users can
access several seismological ML models and benchmark datasets available in the
literature via a single interface. SeisBench is built to be extensible, with
community involvement encouraged to expand the package. Having such frameworks
available for accessing leading ML models forms an essential tool for
seismologists seeking to iterate and apply the next generation of ML techniques
to seismic data.
