Automated ML
"How many members of a certain demographic group does it take to perform a specified task?"
"A finite number: one to perform the task and the remainder to act in a manner stereotypical of the group in question." <insert your light bulb joke here>
This is meta humor – the finest type of humor for ensuing hilarity for those who are quantitatively inclined. Similarly, automated ML is a class of meta learning, also known as learning to learn – the idea that you can apply the automation principles to themselves to make the process of gaining insights even faster and more elegant.
Automated ML is the approach and underlying technology of applying certain automation techniques to accelerate the model's development life cycle. Automated ML enables citizen data scientists and domain experts to train ML models, and helps them build optimal solutions to ML problems. It provides a higher level of abstraction for finding out what the best model is, or an ensemble of models suitable for a specific problem. It assists data scientists by automating the mundane and repetitive tasks of feature engineering, including architecture search and hyperparameter optimization. The following diagram represents the ecosystem of automated ML:
Figure 1.2 – Automated ML ecosystem
These three key areas – feature engineering, architecture search, and hyperparameter optimization – hold the most promise for the democratization of AI and ML. Some automated feature engineering techniques that are finding domain-specific usable features in datasets include expand/reduce, hierarchically organizing transformations, meta learning, and reinforcement learning. For architectural search (also known as neural architecture search), evolutionary algorithms, local search, meta learning, reinforcement learning, transfer learning, network morphism, and continuous optimization are employed.
Last, but not least, we have hyperparameter optimization, which is the art and science of finding the right type of parameters outside the model. A variety of techniques are used here, including Bayesian optimization, evolutionary algorithms, Lipchitz functions, local search, meta learning, particle swarm optimization, random search, and transfer learning, to name a few.
In the next section, we will provide a detailed overview of these three key areas of automated ML. You will see some examples of them, alongside code, in the upcoming chapters. Now, let's discuss how automated ML really works in detail by covering feature engineering, architecture search, and hyperparameter optimization.