What does automated machine learning do?

Automated machine learning (AutoML) focuses on simplifying the process of selecting, training, and tuning machine learning models. Its primary function is to automatically search through various algorithms and hyperparameter combinations to identify the best-performing model for a given dataset and problem type.

The automation aspect greatly reduces the need for deep expertise in machine learning, allowing users to generate results faster and with less manual intervention. By evaluating different models and configurations, AutoML can systematically determine which approach yields the highest accuracy or best performance metrics based on the specific context in which it is applied. This is particularly beneficial in scenarios where data scientists may face constraints such as time or expertise.

In contrast, manually training models, creating graphical user interfaces, or developing cloud infrastructures does not align with the primary goal of AutoML. These tasks may play a role in the broader context of machine learning projects but do not encapsulate the core function that AutoML serves.