Glossary

AutoML (automated machine learning)

Tooling that automates parts of building a machine learning model, such as feature selection, algorithm choice and hyperparameter tuning.

AutoML, short for automated machine learning, is tooling that automates parts of building a model that a practitioner would otherwise do by hand: trying different algorithms, generating and selecting features, and searching for good settings through hyperparameter tuning. Given a dataset and a target column, an AutoML tool typically trains and compares many candidate models and returns the best-performing one, evaluated with cross-validation.

The scope of "AutoML" varies by tool: some automate only the model-selection and tuning step, assuming clean, already-engineered inputs, while more complete platforms also automate parts of feature engineering and preprocessing. Most AutoML tools still expect a well-defined, labeled dataset and a clear target to predict; framing the business problem, sourcing the right data and deciding what "good" looks like generally remain human work.

AutoML lowers the skill and time needed to get a reasonable baseline model, which is useful for prototyping or for teams without a dedicated data scientist, and it can reduce the trial-and-error part of a project. Its risks are treating the winning model as automatically production-ready without checking for data leakage or fairness issues, and losing the domain insight a human would gain from manually exploring the data, or the MLOps discipline needed to keep the model healthy after deployment.

Last reviewed September 22, 2026

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Related terms

Hyperparameter tuning The process of finding the settings that control how a model learns, such as learning rate or tree depth, rather than what it learns. Feature engineering Creating, transforming or selecting the input variables a model is trained on, to make the underlying pattern easier for it to learn. Cross-validation A method for estimating how well a model generalizes by repeatedly training and testing it on different splits of the same data. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Data scientist A practitioner who combines statistics, programming and domain knowledge to build models and extract insight from data.

Related tools

AutoGluon Open-source AutoML library from AWS that automates model selection and ensembling, including a time-series forecasting module. Dataiku Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists. DataRobot Enterprise AutoML and generative AI platform for building, deploying, and governing predictive models. Google Vertex AI Google Cloud's unified platform for AutoML, custom model training, and access to foundation models. H2O.ai Open-source machine learning library and framework, paired with commercial enterprise AutoML and MLOps products.

Related guides

How to choose a data science & ML platform The decisions that matter are who builds the models, how automated the modeling should be, and whether you want a cloud-native platform or a portable one.