Summary

The goal of AutoML is to enable domain experts who are not familiar with machine learning technologies to use ML techniques easily. The primary goal is to reduce the steep learning curve and the huge costs of handcrafting machine learning solutions by making the whole end-to-end machine learning pipeline (data preparation, feature engineering, and automatic model generation) more automated.

After reviewing the state-of-the-art solution available at the end of 2022, we discussed how to use Google Cloud AutoML both for text, videos, and images, achieving results comparable to the ones achieved with handcrafted models. AutoML is probably the fastest-growing research topic and interested readers can find the latest results at https://www.automl.org/.

The next chapter discusses the math behind deep learning, a rather advanced topic that is recommended if you are interested in understanding what is going on “under the hood” when you play with neural networks...