AutoML

AutoML, or Automated Machine Learning, refers to the process of automating the end-to-end process of applying machine learning to real-world problems. It streamlines the workflow of model selection, hyperparameter tuning, and feature engineering, making machine learning more accessible to non-experts. Key characteristics include automated data preprocessing, model training, and evaluation, which significantly reduces the time and expertise required. Common use cases include predictive analytics, classification tasks, and time series forecasting, enabling businesses to leverage machine learning without extensive technical knowledge.