Automated machine learning

Automated machine learning (AutoML) refers to the process of automating the end-to-end application of machine learning to real-world problems. It encompasses tasks such as data preprocessing, feature selection, model selection, hyperparameter tuning, and model evaluation, aiming to make machine learning accessible to non-experts and improve workflow efficiency. AutoML is particularly beneficial for data scientists, business analysts, and organizations looking to leverage machine learning without extensive expertise in the field.