The Springer Series on Challenges in Machine Learning
The past decade has seen an explosion of machine learning research and appli-
cations; especially, deep learning methods have enabled key advances in many
applicationdomains,suchas computervision,speechprocessing,andgameplaying.
However, the performance of many machine learning methods is very sensitive
to a plethora of design decisions, which constitutes a considerable barrier for
new users. This is particularly true in the booming field of deep learning, where
human engineers need to select the right neural architectures, training procedures,
regularization methods, and hyperparameters of all of these components in order to
make their networks do what they are supposed to do with sufficient performance.
This process has to be repeated for every application. Even experts are often left
with tedious episodes of trial and error until they identify a good set of choices for
a particular dataset.
The field of automatedmachinelearning(AutoML)aims to makethese decisions
in a data-driven, objective, and automated way: the user simply provides data,
and the AutoML system automatically determines the approach that performs best
for this particular application. Thereby, AutoML makes state-of-the-art machine
learning approaches accessible to domain scientists who are interested in applying
machine learning but do not have the resources to learn about the technologies
behind it in detail. This can be seen as a democratization of machine learning: with
AutoML, customized state-of-the-art machine learning is at everyone’s fingertips.
As we show in this book, AutoML approaches are already mature enough to
rival and sometimes even outperform human machine learning experts. Put simply,
AutoML can lead to improved performance while saving substantial amounts of
time and money, as machine learning experts are both hard to find and expensive.
As a result, commercial interest in AutoML has grown dramatically in recent years,
and several major tech companies are now developing their own AutoML systems.
We note,though,thatthepurposeofdemocratizingmachinelearningis servedmuch
betterby open-sourceAutoMLsystems than byproprietarypaidblack-boxservices