  We introduce the Automatic Learning for the Rapid Classification of Events
(ALeRCE) broker, an astronomical alert broker designed to provide a rapid and
self--consistent classification of large etendue telescope alert streams, such
as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the
Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a
Chilean--led broker run by an interdisciplinary team of astronomers and
engineers, working to become intermediaries between survey and follow--up
facilities. ALeRCE uses a pipeline which includes the real--time ingestion,
aggregation, cross--matching, machine learning (ML) classification, and
visualization of the ZTF alert stream. We use two classifiers: a stamp--based
classifier, designed for rapid classification, and a light--curve--based
classifier, which uses the multi--band flux evolution to achieve a more refined
classification. We describe in detail our pipeline, data products, tools and
services, which are made public for the community (see
\url{https://alerce.science}). Since we began operating our real--time ML
classification of the ZTF alert stream in early 2019, we have grown a large
community of active users around the globe. We describe our results to date,
including the real--time processing of $9.7\times10^7$ alerts, the stamp
classification of $1.9\times10^7$ objects, the light curve classification of
$8.5\times10^5$ objects, the report of 3088 supernova candidates, and different
experiments using LSST-like alert streams. Finally, we discuss the challenges
ahead to go from a single-stream of alerts such as ZTF to a multi--stream
ecosystem dominated by LSST.
