AutoML, or Automated Machine Learning, refers to tools and processes that automate parts of building and deploying machine learning models. Depending on the platform, AutoML can handle tasks such as data preparation, feature selection, algorithm selection, model training, hyperparameter tuning, and performance evaluation. This reduces the amount of manual experimentation required to develop a suitable model for a particular dataset and prediction task.
AutoML is useful when organizations need to develop machine learning solutions without manually testing every modeling approach. For example, it can compare different models to predict equipment failures, customer churn, demand, or fraud and identify a strong-performing candidate. AutoML does not eliminate the need for data quality checks, domain expertise, or model governance.
