Abstract: The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is an alert-broker system applying data science and machine learning techniques to classify objects discovered by the Zwicky Transient Facility (ZTF) in real-time. I will describe our infrastructure, machine learning stages and public interface. In particular, I will detail how we are using deep-learning to enable classification from sparse, early-time data and facilitate rapid follow-up studies. Our value-added data products are available to astrophysicists and can be used to enable new science. We briefly discuss future plans, focusing on scaling from ZTF to the alert volume we expect with the Large Synoptic Survey Telescope (LSST).