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Machine Learning (ML) methods have seen widespread adoption in seismology inrecent years. The ability of these techniques to efficiently infer thestatistical properties of large datasets often provides significantimprovements over traditional techniques. With the entire spectrum ofseismological tasks, e.g., seismic picking, source property estimation, groundmotion prediction, hypocentre determination; among others, now incorporating MLapproaches, numerous models are emerging as these techniques are furtheradopted within seismology. To evaluate these algorithms, quality controlledbenchmark datasets that contain representative class distributions are vital.In addition to this, models require implementation through a common frameworkto facilitate comparison. Accessing these various benchmark datasets fortraining and implementing the standardization of models is currently atime-consuming process, hindering further advancement of ML techniques withinseismology. These development bottlenecks also affect "practitioners" seekingto deploy the latest models on seismic data, without having to necessarilylearn entirely new ML frameworks to perform this task. We present SeisBench asa software package to tackle these issues. SeisBench is an open-sourceframework for deploying ML in seismology. SeisBench standardises access to bothmodels and datasets, whilst also providing a range of common processing anddata augmentation operations through the API. Through SeisBench, users canaccess several seismological ML models and benchmark datasets available in theliterature via a single interface. SeisBench is built to be extensible, withcommunity involvement encouraged to expand the package. Having such frameworksavailable for accessing leading ML models forms an essential tool forseismologists seeking to iterate and apply the next generation of ML techniquesto seismic data.

SeisBench -- A Toolbox for Machine Learning in Seismology