With the recent popularity of machine learning algorithms such as neural networks and ensemble methods, etc., machine learning models become more like a 'black box', harder to understand and interpret. To gain the stakeholders' trust, there is a strong need to develop tools and methodologies to help the user to understand and explain how predictions are made. In this video, you learn about our open source Machine Learning Interpretability toolkit, InterpretML, which incorporates the cutting-edge technologies developed by Microsoft and leverages proven third-party libraries. InterpretML introduces a state-of-the-art glass box model (EBM), and provides an easy access to a variety of other glass box models and blackbox explainers.