  Active Galaxies with a jet pointing towards us, so-called blazars, play an
important role in the field of high-energy astrophysics. One of the most
important features in the classification scheme of blazars is the peak
frequency of the synchrotron emission ($\nu_{\rm peak}^S$) in the spectral
energy distribution (SED). In contrast to standard blazar catalogs that usually
calculate the $\nu_{\rm peak}^S$ manually, we have developed a machine-learning
algorithm - BlaST - that not only simplifies the estimation, but also provides
a reliable uncertainty evaluation. Furthermore, it naturally accounts for
additional SED components from the host galaxy and the disk emission, which may
be a major source of confusion. Using our tool, we re-estimate the synchrotron
peaks in the Fermi 4LAC-DR2 catalog. We find that BlaST, improves the $\nu_{\rm
peak}^S$ estimation especially in those cases where the contribution of
components not related to the jet is important.
