The potential of machine learning in industrial applications is huge. But a big challenge in many projects is the availability of sufficient training data. Often the large number of product variants means that for a given variant only a handful of examples are available. Sometimes the parameters of interest are seldom changed, which decreases the amount of information in the training data. To enhance such data we need to include process knowledge. A natural framework for this are Bayesian networks. Guided by examples Maksim will explain the basic principles of how you can encode process knowledge in a Bayesian network, how this network is trained and how it helps you to make accurate predictions or to optimize an industrial process.