TODS is a full-stack ML pipeline for time-series outlier detection, providing modules for data preprocessing, time-series transformation, feature extraction (time/frequency domains), and detection algorithms including point-wise, pattern-wise, and system-wise anomaly detection. It supports AutoML-style pipelines with human-in-the-loop calibration, and includes deep learning models like DeepLog alongside classical ML methods from PyOD and various ensemble techniques.