Due to the high complexity of modern internal combustion engines and powertrain systems, the proper calibration of the electronic control unit’s (ECU) parameters has a strong impact on project targets like fuel consumption, emissions and drivability, as well as development costs and project duration. Simulation methods representing the system behavior with a model can support the calibration process considerably. However, standard physics-based models are often not able to describe all effects with sufficient accuracy, or the effort to set up a detailed model is too high. Physics-based models can also have a high computational demand, so that their simulation is not real-time capable. More suited for ECU calibration are data-driven models, combined with Design of Experiment (DoE). The system to be calibrated is identified with few specific test bench or vehicle measurements. From these measurements, a mathematical regression model is built. This paper describes recently developed machine learning methods based on Gaussian processes. In contrast to polynomial models or neural network regression, Gaussian processes are able to model strongly nonlinear systems with high accuracy, and are robust against measurement noise and outliers. No expert knowledge is required for their practical application, all model parameters are determined automatically by probabilistic principles. The data-driven model replaces the real engine or vehicle in the calibration process, and combined with optimization methods, the best set of ECU parameters with respect to the project targets is identified. The short response time of Gaussian process models further enables their use in real-time environments, e.g. Hardware-in-the-Loop (HiL) test systems or even directly on the ECU. This paper shows the application of the datadriven approach in the calibration process on several examples.