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An introduction to AutoML
That is probably a good intuition to start with. The problem is that it is not easy to
explain to the users of your model why a particular composition of CNNs works
well within the breast cancer detection domain. Ideally, you want to provide
easily accessible deep learning tools to the domain experts (in this case, medical
professionals) without such a tool requiring a strong machine learning background.
The other problem is that it is not easy to understand whether or not there are
variants (for example, different compositions) of the original manually crafted
model that can achieve better results. Ideally, you want to provide deep learning
tools for exploring the space of variants (for example, different compositions) in a
more principled and automatic way.
So, the central idea of AutoML is to reduce the steep learning curve and the huge
costs of handcrafting machine learning solutions by making the whole end-toend
machine learning pipeline more automated. To this end, we assume that
the AutoML pipeline consists of three macro-steps: data preparation, feature
engineering, and automatic model generation (see Figure 1). Throughout the initial
part of this chapter, we are going to discuss these three steps in detail. Then, we
will focus on Cloud AutoML:
Figure 1: Three steps of an AutoML pipeline
Achieving AutoML
How can AutoML achieve the goal of end-to-end automatization? Well, you are
probably already guessing that a natural choice is to use machine learning – that's
very cool – AutoML uses ML for automating ML pipelines.
What are the benefits? Automating the creation and tuning of the machine learning
end-to-end offers produces simpler solutions, reduces the time to produce them,
and ultimately might produce architectures that could potentially outperform the
models that were crafted by hand.
Is this a closed research area? Quite the opposite. At the beginning of 2020, AutoML
is a very open research field, which is not surprising, as the initial paper drawing
attention to AutoML was published at the end of 2016.
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