The widespread use of machine learning algorithms and the high level of expertise required to utilize them have fuelled the demand for solutions that can be used by non-experts. Automated machine learning (AutoML) is the process of designing and applying end-to-end ML models for real-world problems, easing the need for expertise. In this talk, I will present our meta-learning algorithm, AutoGRD, for model selection based on a graphical embedded meta-features of the training set. Also, I will discuss the evaluation that we made and show that AutoGRD outperforms state-of-the-art meta-learning and Bayesian methods.