  The engineering of machine learning systems is still a nascent field; relying
on a seemingly daunting collection of quickly evolving tools and best
practices. It is our hope that this guidebook will serve as a useful resource
for machine learning practitioners looking to take advantage of Bayesian
optimization techniques. We outline four example machine learning problems that
can be solved using open source machine learning libraries, and highlight the
benefits of using Bayesian optimization in the context of these common machine
learning applications.
