The stages of the ML lifecycle typically include data collection and preprocessing, feature engineering, model training and validation, model evaluation, deployment, monitoring, and maintenance. These stages encompass the essential processes involved in developing, deploying, and managing machine learning models to ensure their accuracy, reliability, and performance in real-world applications.