What is AutoML?
The main task in the modeling phase is to select the different models to be evaluated and adjust the different hyperparameters of each one. This work that data scientists normally perform requires a lot of time as well as experienced professionals. From a computational point of view, hyperparameter tuning is a comprehensive search process, so it can be automated.
AutoML is a process that automates, using AI algorithms, every step of the ML pipeline described previously, from the data preprocessing to the deployment of the ML model, allowing non-data scientists (such as software developers) to use ML techniques without the need for experience in the field. In the following figure, we can see a simple representation of the inputs and outputs of an AutoML system:
Figure 1.4 – How AutoML works
AutoML is also capable of producing simpler solutions, more agile proof-of-concept creation, and unattended training of models that often outperform those created manually, dramatically improving the predictive performance of the model and allowing data scientists to perform more complex tasks that are more difficult to automate, such as data preprocessing and feature engineering, defined in the Model monitoring section. Before introducing the AutoML types, let's take a quick look at the main differences between AutoML and traditional ML.
Differences from the standard approach
In the standard ML approach, data scientists have an input dataset to train. Usually, this raw data is not ready for the training algorithms, so an expert must apply different methods, such as data preprocessing, feature engineering, and feature extraction methods, as well as model tuning through algorithm selection and hyperparameter optimization, to maximize the model's predictive performance.
All of these steps are time-consuming and resource-intensive, being the main obstacle to putting ML into practice.
With AutoML, we simplify these steps for non-experts, making it possible to apply ML to solve a problem in an easier and faster way.
Now that the main concepts of AutoML have been explained, we can put them into practice. But first, we will see what the main types of AutoML are and some of the widely used tools to perform AutoML.