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AutoML is an emerging field in machine learning that focuses on automating the end-to-end process of applying machine learning to real-world problems. The goal of AutoML is to make machine learning more accessible and efficient by automating the time-consuming and iterative tasks involved in developing machine learning models, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation. By employing various techniques, including meta-learning, transfer learning, and neural architecture search, AutoML systems aim to discover the most suitable machine learning pipelines and models for a given dataset and problem, often achieving performance comparable to or even surpassing models designed by human experts. This automation not only accelerates the model development process but also democratizes machine learning by enabling domain experts and non-machine learning practitioners to leverage the power of machine learning without requiring extensive knowledge of the underlying algorithms and techniques. As the field of AutoML continues to evolve, it has the potential to significantly impact various domains, from scientific research to business applications, by enabling the rapid development and deployment of high-quality machine learning solutions

automated machine learning, neural architecture search, hyperparameter optimization, meta-learning, model selection, feature engineering, machine learning pipelines

Robert Anderson

CITATION : "Robert Anderson. 'AutoML.' Design+Encyclopedia. (Accessed on April 15, 2024)"

AutoML Definition
AutoML on Design+Encyclopedia

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