  While halide perovskites attract significant academic attention, examples of
at-scale industrial production are still sparse. In this perspective, we review
practical challenges hindering the commercialization of halide perovskites, and
discuss how machine-learning (ML) tools could help: (1) active-learning
algorithms that blend institutional knowledge and human expertise could help
stabilize and rapidly update baseline manufacturing processes; (2) ML-powered
metrology, including computer imaging, could help narrow the performance gap
between large- and small-area devices; and (3) inference methods could help
accelerate root-cause analysis by reconciling multiple data streams and
simulations, focusing research effort on areas with highest probability for
improvement. We conclude that to satisfy many of these challenges, incremental
-- not radical -- adaptations of existing ML and statistical methods are
needed. We identify resources to help develop in-house data-science talent, and
propose how industry-academic partnerships could help adapt "ready-now" ML
tools to specific industry needs, further improve process control by revealing
underlying mechanisms, and develop "gamechanger" discovery-oriented algorithms
to better navigate vast materials combination spaces and the literature.
