AutoML (Automated Machine Learning) is an approach in machine learning that focuses on automating the process of building and deploying machine learning models. AutoML aims to make machine learning more accessible to a wider range of users, including those who lack extensive machine learning expertise, by automating many of the repetitive and time-consuming tasks involved in model development.

AutoML systems typically incorporate a range of techniques, including hyperparameter optimization, feature selection, model selection, and architecture search. These techniques are combined in various ways to create an end-to-end pipeline that automates the entire machine learning process, from data preprocessing to model deployment.

The key benefit of AutoML is that it enables users to create and deploy high-quality machine learning models without having to invest significant time and resources in model development. AutoML systems can be used to address a wide range of machine learning problems, including classification, regression, and clustering, and can be applied across a variety of industries, including healthcare, finance, and retail.