In this "Perspective", we discuss the potential of combining DoE and machine learning, i.e. active machine learning. Olsson defines active machine learning as a supervised machine learning technique in which the learner, being the machine learning model, is in control of the data from which it learns . With active machine learning, machine learning algorithms are used to iteratively determine new experimental data, so-called training data, based on uncertainty criteria. Note that "experimental" can also refer to computationally expensive highlevel simulations e.g. high level ab initio calculations of molecular properties or large eddy simulations of reactive flow with Computational Fluid Dynamics (CFD) codes . Active machine learning consists of two branches with two different purposes: active learning and Bayesian optimization. Active learning aims to explore and model a process with a minimum of "experiments" to ensure accurate predictions over the entire design space . Bayesian optimization is essentially a machine learning-based optimization strategy, where iteratively new experimental data is selected to find the experiment which optimizes the objective .