We illustrate the ability of the stochastic gradient variational Bayes algorithm, which is a very popular machine learning tool, to work with macrodata and macromodels. Choosing two approximations (mean-field and normalizing flows), we test properties of algorithms for a set of models and show that these models can be estimated fast despite the presence of estimated hyperparameters. Finally, we discuss the difficulties and possible directions of further research.