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An introduction to AutoML
Put simply, we can say that Google Cloud ML is very focused on simplicity of use
and efficiency for AutoML. Let's summarize the main steps required (see Figure 22):
1. The dataset is imported
2. Your dataset schema and labels are defined
3. The input features are automatically recognized
4. AutoML performs the magic by automatically doing feature engineering,
creating a model, and tuning the hyperparameters
5. The automatically built model can then be evaluated
6. The model is then deployed in production
Of course, it is possible to repeat in cycle 2-6 by changing the schema and the
definition of the labels:
Figure 22: AutoML Table – main steps required
In this section we have seen an example of AutoML focused on easy of use and
efficiency. The progress made is shown in Faes et al. [7], quoting the paper:
"We show, to our knowledge, a first of its kind automated design and
implementation of deep learning models for health-care application by non-AI
experts, namely physicians. Although comparable performance to expert-tuned
medical image classification algorithms was obtained in internal validations
of binary and multiple classification tasks, more complex challenges, such as
multilabel classification, and external validation of these models was insufficient.
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