AutoML is the automation of tasks involved in building, tuning, evaluating, and sometimes deploying machine learning models. It may automate model selection, hyperparameter tuning, data preparation, feature engineering, and pipeline construction. AutoML can improve productivity for experts and make model development more accessible to non-specialists, while still requiring validation and governance.
Any automated process for building machine learning models. AutoML can automatically do tasks such as the following: - Search for the most appropriate model. - Tune hyperparameters. - Prepare data (including performing feature engineering). - Deploy the resulting model. AutoML is useful for data scientists because it can save them time and effort in developing machine learning pipelines and improve prediction accuracy. It is also useful to non-experts, by making complicated machine learning tasks more accessible to them. See Automated Machine Learning (AutoML) in Machine Learning Crash Course for more information.
Machine learning algorithms that "learn to learn" through black-box optimization. For more information, see ML Glossary.