SBI flow chart. From a set of priors we simulate a sample of stellar abundances using CHEMPY (Rybizki et al. 2017; Philcox & Rybizki 2019), which we use to train a neural network emulator to speed up the data generation process. Using the neural network emulator we produce training data to train the Neural Density Estimator. With this we infer the posterior distribution of the model parameters from a single star. Repeating that for N_stars from the same galaxy gives an accurate fit of the IMF slope and Type Ia supernovae normalization.