  As more and more organizations rely on data-driven decision making,
large-scale analytics become increasingly important. However, an analyst is
often stuck waiting for an exact result. As such, organizations turn to Cloud
providers that have infrastructure for efficiently analyzing large quantities
of data. But, with increasing costs, organizations have to optimize their
usage. Having a cheap alternative that provides speed and efficiency will go a
long way. Concretely, we offer a solution that can provide approximate answers
to aggregate queries, relying on Machine Learning (ML), which is able to work
alongside Cloud systems. Our developed lightweight ML-led system can be stored
on an analyst's local machine or deployed as a service to instantly answer
analytic queries, having low response times and monetary/computational costs
and energy footprint. To accomplish this we leverage the knowledge obtained by
previously answered queries and build ML models that can estimate the result of
new queries in an efficient and inexpensive manner. The capabilities of our
system are demonstrated using extensive evaluation with both real and synthetic
datasets/workloads and well known benchmarks.
