Summary
Today, the success of ML within an enterprise largely depends on human ML experts who can construct business-specific features and workflows. Automated ML aims to change this, as it aims to automate ML so as to provide off-the-shelf ML methods that can be utilized without expert knowledge. To understand how automated ML works, we need to review the underlying four subfields, or pillars, of automated ML: hyperparameter optimization; automated feature engineering; neural architecture search; and meta-learning.
In this chapter, we explained what is under the hood in terms of the technologies, techniques, and tools used to make automated ML possible. We hope that this chapter has introduced you to automated ML techniques and that you are now ready to do a deeper dive into the implementation phase.
In the next chapter, we will review the open source tools and libraries that implement these algorithms to get a hands-on overview of how to use these concepts in practice, so...
