  Data quality monitoring is critical to all experiments impacting the quality
of any physics results. Traditionally, this is done through an alarm system,
which detects low level faults, leaving higher level monitoring to human crews.
Artificial Intelligence is beginning to find its way into scientific
applications, but comes with difficulties, relying on the acquisition of new
skill sets, either through education or acquisition, in data science. This
paper will discuss the development and deployment of the Hydra monitoring
system in production at Gluex. It will show how "off-the-shelf" technologies
can be rapidly developed, as well as discuss what sociological hurdles must be
overcome to successfully deploy such a system. Early results from production
running of Hydra will also be shared as well as a future outlook for
development of Hydra.
