AutoML

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.

Practitioners refer to automl when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.