The joint progress of artificial neural networks and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research.This development is accompanied by a rapid growth of the required computational demands for larger models and more data.Concurrently, emerging properties of foundation models such as in-context learning drive new opportunities for machine learning applications.However, the computational cost of such applications is a limiting factor of the technology in data-centers, and more importantly in mobile devices and edge systems.To mediate the energy footprint and non-trivial latency of contemporary systems, neuromorphic computing systems deeply integrate computational principles of neurobiological systems by leveraging low-power analog and digital technologies.SpiNNaker2 is a digital neuromorphic chip developed for scalable machine learning.The event-based and asynchronous design of SpiNNaker2 allows the composition of large-scale systems from thousands of chips.In this work, we present the design and operating principles of SpiNNaker2 systems.Furthermore, we outline a number of machine learning applications that we developed on either the full chip or earlier prototypes.The already available applications range from accelerating artificial neural networks over bio-inspired spiking neural networks to generalized event-based neural networks.With the successful development and deployment of SpiNNaker2, we aim to facilitate the advancement of event-based and asynchronous algorithms for future generations of machine learning systems.