Building machine learning models has become as easy as push of a button, especially with a tool like DataRobot. But which model is the right one to choose? In addition to accuracy and calculation speed, interpretability is commonly referred to as a deciding factor in allowing ML models to be used in real life application. In this talk, I will illustrate innovative methods to elucidate and explain learned by models and reasoning behind predictions made by them.