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AutoML

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TL;DR. Automated processes that build machine learning models, handling tasks like model selection, hyperparameter tuning, and data preparation.

Technical Definition

Automated processes that build machine learning models, handling tasks like model selection, hyperparameter tuning, and data preparation.

How it works

AutoML refers to the automation of building, training, and deploying machine learning models. It can perform tasks such as selecting the best model architecture, optimizing hyperparameters, and preparing data, often making complex ML tasks more accessible. This saves time for data scientists and enables non-experts to leverage machine learning.

Related Concepts

  • Hyperparameter optimization — The process of finding the best set of hyperparameters for a machine learning model to achieve optimal performance.

Further Reading

  • Google ML Glossary