Machine learning interatomic potentials (MLIPs) enable molecular simulations at longer time scales without compromising accuracy and at lower computational costs compared to electronic structure methods such as density functional theory (DFT). Application of MLIPs to complex functional-material development can help to create new scientific insights, however, MLIPs need ad-hoc training for each new system. Reaching sufficient accuracy through large-scale training is data-intensive, and requires a high level of technical proficiency from the user. Reliable MLIP construction requires an appropriate selection of representative structures and calibrated model uncertainty, while avoiding undersampling of the state space. Currently, there is a lack end-to-end automated software to take this complexity away from the end user. In this tutorial, we show how to use CURATOR, an open-source software-based autonomous batch active learning workflow. CURATOR trains message-passing graph neural networks and enables management of model training, production testing, data selection based on uncertainty estimation, optimal batch choice, labeling via DFT-based simulations, and retraining in a user-friendly way.