  Seismology has witnessed significant advancements in recent years with the
application of deep learning methods to address a broad range of problems.
These techniques have demonstrated their remarkable ability to effectively
extract statistical properties from extensive datasets, surpassing the
capabilities of traditional approaches to an extent. In this study, we present
SAIPy, an open source Python package specifically developed for fast data
processing by implementing deep learning. SAIPy offers solutions for multiple
seismological tasks, including earthquake detection, magnitude estimation,
seismic phase picking, and polarity identification. We introduce upgraded
versions of previously published models such as CREIMERT capable of identifying
earthquakes with an accuracy above 99.8 percent and a root mean squared error
of 0.38 unit in magnitude estimation. These upgraded models outperform state of
the art approaches like the Vision Transformer network. SAIPy provides an API
that simplifies the integration of these advanced models, including CREIMERT,
DynaPickerv2, and PolarCAP, along with benchmark datasets. The package has the
potential to be used for real time earthquake monitoring to enable timely
actions to mitigate the impact of seismic events. Ongoing development efforts
aim to enhance the performance of SAIPy and incorporate additional features
that enhance exploration efforts, and it also would be interesting to approach
the retraining of the whole package as a multi-task learning problem.
