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An introduction to AutoML
The goal of AutoML is to enable domain experts who are unfamiliar with machine
learning technologies to use ML techniques easily.
In this chapter, we will go through a practical exercise using Google Cloud, and do
quite a bit of hands-on work after briefly discussing the fundamentals. We will talk
about automatic data preparation, automatic feature engineering, and automatic
model generation. Then, we introduce AutoKeras and Cloud AutoML with its
multiple solutions for Table, Vision, Text, Translation, and for Video processing.
What is AutoML?
During the previous chapters we have introduced several models used in modern
machine learning and deep learning. For instance, we have seen architectures such
as Dense networks, CNNs, RNNs, Autoencoders, and GANs.
Two observations are in order. First, these architectures are manually designed
by deep learning experts, and are not necessarily easy to explain to non-experts.
Second, the composition of these architectures themselves was a manual process,
which involved a lot of human intuition and trial and error.
Today, one primary goal of artificial intelligence research is to achieve Artificial
General Intelligence (AGI) – the intelligence of a machine that can understand
and automatically learn any type of work or activity that a human being can do.
However, the reality was very different before AutoML research and industrial
applications started. Indeed, before AutoML, designing deep learning architectures
was very similar to crafting – the activity or hobby of making decorative articles
by hand.
Take for instance the task of recognizing breast cancer from X-rays. After reading the
previous chapters, you will probably think that a deep learning pipeline created by
composing several CNNs may be an appropriate tool for this purpose.
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