  Machine learning techniques offer a plethora of opportunities in tackling big
data within the astronomical community. We present the set of Generalized
Linear Models as a fast alternative for determining photometric redshifts of
galaxies, a set of tools not commonly applied within astronomy, despite being
widely used in other professions. With this technique, we achieve catastrophic
outlier rates of the order of ~1%, that can be achieved in a matter of seconds
on large datasets of size ~1,000,000. To make these techniques easily
accessible to the astronomical community, we developed a set of libraries and
tools that are publicly available.
