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Automated machine learning (AutoML)

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TL;DR. Automating the process of building and optimizing machine learning models.

Technical Definition

Automating the process of building and optimizing machine learning models.

How it works

Automated Machine Learning (AutoML) is a field focused on automating the end-to-end process of applying machine learning to real-world problems. It aims to make ML more accessible by automating tasks like data preprocessing, model selection, hyperparameter tuning, and deployment, thereby improving efficiency and performance.

Related Concepts

  • Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
  • Hyperparameter Tuning — The process of finding optimal configuration values that control model training, such as learning rate, batch size, and architecture choices.
  • Machine Learning — A field of AI where systems learn patterns from data instead of following hard-coded rules.

Further Reading

  • Wikipedia — Glossary of AI