The possibilities of using machined learning models to support or even replace traditional models in solid mechanics have inspired a plethora of new ideas and research directions. For example, in data-driven mechanics, completely new formulations of mechanical equilibrium under non-linear material responses have been introduced. Other works are seeking to replace constitutive models, which are commonly formulated in terms of closed-form algebraic equations or as ordinary differential equations, by trained machine learning algorithms. Finally, there is a number of publications, where numerical simulations are replaced altogether by trained machine learning models acting as numerically efficient surrogate models for specific problems. This presentation will provide an overview on ongoing efforts and current trends for applications of machine learning on mechanical systems and also provide examples for data-oriented constitutive modeling and surrogate models for inverse analysis.