If we wanted to summarize the machine learning process using just one word, it would certainly be models. This is because what we build with machine learning are abstractions or models representing and simplifying reality, allowing us to solve real-life problems based on a model that we have trained on.
The task of choosing which model to use is becoming increasingly difficult, given the increasing number of models appearing almost every day, but you can make general approximations by grouping methods by the type of task you want to perform and also the type of input data, so that the problem is simplified to a smaller set of options.
