Machine learning automation, or AutoML, is a process that automates the design, training, optimization, and deployment of machine learning models, making it accessible even to those without deep expertise. AutoML streamlines the lengthy and complex steps involved in creating machine learning models, such as data preprocessing, model training, and testing, thereby saving time and reducing costs while improving productivity and model performance. It supports various data types, including image, video, tabular, and textual data, catering to different purposes such as fraud detection, natural language processing, and time-series forecasting. AutoML also plays a significant role in automation testing by enhancing tasks like test case generation, defect prediction, and visual testing. Prominent tools like Google Cloud AutoML, Amazon SageMaker Autopilot, and Azure Machine Learning facilitate this automation, democratizing machine learning by enabling non-experts to build effective models without manual intervention.