PiML provides an integrated Python environment for interpretable machine learning, supporting model development and validation workflows. It offers low-code APIs and interactive tools for training interpretable models, analyzing model behavior, and assessing model robustness and reliability. The toolbox wraps various model types including boosted trees, neural networks, and statistical models, and includes utilities for external black-box model validation.