  Clouds gather a vast volume of telemetry from their networked systems which
contain valuable information that can help solve many of the problems that
continue to plague them. However, it is hard to extract useful information from
such raw data. Machine Learning (ML) models are useful tools that enable
operators to either leverage this data to solve such problems or develop
intuition about whether/how they can be solved. Building practical ML models is
time-consuming and requires experts in both ML and networked systems to tailor
the model to the system/network (a.k.a "domain-customize" it). The number of
applications we deploy exacerbates the problem. The speed with which our
systems evolve and with which new monitoring systems are deployed (deprecated)
means these models often need to be adapted to keep up. Today, the lack of
individuals with both sets of expertise is becoming one of the bottlenecks for
adopting ML in cloud operations. This paper argues it is possible to build a
domain-customized automated ML framework for networked systems that can help
save valuable operator time and effort.
