Automated machine learning (AutoML) is an AI-based solution designed to streamline the entire process of applying machine learning to real-world problems. By automating tasks from raw data ingestion to model deployment, AutoML aims to make sophisticated machine learning accessible to non-experts. This approach simplifies complex steps like data preparation, feature engineering, algorithm selection, and hyperparameter optimization, which traditionally require significant manual effort and expertise. As a result, AutoML not only accelerates solution development but often yields models that outperform hand-designed ones. This innovation significantly broadens the accessibility and efficiency of machine learning, making it a crucial component in automating data science.