  The Zwicky Transient Facility (ZTF) has been observing the entire northern
sky since the start of 2018 down to a magnitude of 20.5 ($5 \sigma$ for 30s
exposure) in $g$, $r$, and $i$ filters. Over the course of two years, ZTF has
obtained light curves of more than a billion sources, each with 50-1000 epochs
per light curve in $g$ and $r$, and fewer in $i$. To be able to use the
information contained in the light curves of variable sources for new
scientific discoveries, an efficient and flexible framework is needed to
classify them. In this paper, we introduce the methods and infrastructure which
will be used to classify all ZTF light curves. Our approach aims to be flexible
and modular and allows the use of a dynamical classification scheme and labels,
continuously evolving training sets, and the use of different machine learning
classifier types and architectures. With this setup, we are able to
continuously update and improve the classification of ZTF light curves as new
data becomes available, training samples are updated, and new classes need to
be incorporated.
