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Automatic data preparation
The first stage of a typical machine learning pipeline deals with data preparation
(recall the pipeline of Figure 1). There are two main aspects that should be taken
into account: data cleansing, and data synthesis.
Chapter 14
Data cleansing is about improving the quality of data by checking for wrong data
types, missing values, errors, and by applying data normalization, bucketization,
scaling, and encoding. A robust AutoML pipeline should automate all of these
mundane but extremely important steps as much as possible.
Data synthesis is about generating synthetic data via augmentation for training,
evaluation, and validation. Normally, this step is domain-specific. For instance, we
have seen how to generate synthetic CIFAR10-like images (Chapter 4, Convolutional
Neural Networks) by using cropping, rotation, resizing, and flipping operations. One
can also think about generate additional images or video via GANs (see Chapter
6, Generative Adversarial Networks) and using the augmented synthetic dataset for
training. A different approach should be taken for text, where it is possible to train
RNNs (Chapter 9, Autoencoders) to generate synthetic text or to adopt more NLP
techniques such as BERT, seq2seq, or Transformers to annotate or translate text
across languages and then translate back to the original one – another domainspecific
form of augmentation.
A different approach is to generate synthetic environments where machine learning
can occur. This became very popular in reinforcement learning and gaming,
especially with toolkits such as OpenAI Gym, which aims to provide an easy-to-setup
simulation environment with a variety of different (gaming) scenarios.
Put simply, we can say that synthetic data generation is another option that should
be provided by AutoML engines. Frequently, the tools used are very domainspecific
and what works for image or video would not necessary work in other
domains such as text. Therefore, we need a (quite) large set of tools for performing
synthetic data generation across domains.
Automatic feature engineering
Featuring engineering is the second step of a typical machine learning pipeline (see
Figure 1). It consists of three major steps: feature selection, feature construction, and
feature mapping. Let's look at each of them in turn:
Feature selection aims at selecting a subset of meaningful features by discarding
those that are providing little contribution to the learning task. In this context,
meaningful is truly dependent on the application and the domain of your specific
problem.
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