  Machine learning methods based on normalizing flows have been shown to
address important challenges, such as critical slowing-down and topological
freezing, in the sampling of gauge field configurations in simple lattice field
theories. A critical question is whether this success will translate to studies
of QCD. This Proceedings presents a status update on advances in this area. In
particular, it is illustrated how recently developed algorithmic components may
be combined to construct flow-based sampling algorithms for QCD in four
dimensions. The prospects and challenges for future use of this approach in
at-scale applications are summarized.
