This paper addresses the problem of predictive maintenance in industry 4.0. Industry 4.0 revolutionized companies in the way they produce, manufacture, improve and distribute products. Industries are competing to implement and develop digital technologies driving Industry 4.0 which leads to increased automation (integration of advanced sensors, embedded software and robotics that collect and analyse data and allow for better decision making), predictive maintenance, self-optimization of process improvements and, above all, a new level of efficiencies and responsiveness to customers not previously possible. From this context the goal of the proposed work is to provide an industrial use case of machine smartifying to predict its Remaining Useful Life based on internal and external data collection and analysis using a Machine Learning algorithms. A digital Twin dashboard for real time monitoring of the machine and the result of the Machine Learning model prediction will be presented.