Explaining Machine Learning models have become black box solutions. In the past two years, many tools and python packages have been developed to deliver more insights into understanding and explaining the outcome of the ML model and why it behaved the way it has. Addressing the questions like: how sure can we be, if the prediction is correct, what information does the accuracy matrix hold, how different would have the outcome been, had it have different input parameters set. Introducing also the principles of ML, such as fairness of models, accountability, and transparency. Demos will include Azure Machine Learning service and different Python packages to address topics of explainability and interpretability of models.