Advancements in machine-learning (ML) techniques are driving a paradigm shift in image
processing. Flow diagnostics with optical techniques is not an exception. Considering the
existing and foreseeable disruptive developments in flow field measurement techniques, we
elaborate this perspective, particularly focused to the field of particle image velocimetry. The
driving forces for the advancements in ML methods for flow field measurements in recent years
are reviewed in terms of image preprocessing, data treatment and conditioning. Finally, possible
routes for further developments are highlighted.Stefano Discetti acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 949085). Yingzheng Liu acknowledges financial support from the National Natural Science Foundation of China (11725209)
