  The Zwicky Transient Facility (ZTF), a state-of-the-art optical robotic sky
survey, registers on the order of a million transient events - such as
supernova explosions, changes in brightness of variable sources, or moving
object detections - every clear night, and generates associated real-time
alerts. We present Alert-Classifying Artificial Intelligence (ACAI), an
open-source deep-learning framework for the phenomenological classification of
ZTF alerts. ACAI uses a set of five binary classifiers to characterize objects
which, in combination with the auxiliary/contextual event information available
from alert brokers, provides a powerful tool for alert stream filtering
tailored to different science cases, including early identification of
supernova-like and anomalous transient events. We report on the performance of
ACAI during the first months of deployment in a production setting.
