Abstract Tectonic tremor is a weak, long‐duration seismic signal often observed in subduction zones and on some other plate‐bounding faults. Because of tremor's characteristically low amplitude (and low signal‐to‐noise) and lack of clear phase arrivals, detecting and locating tremor usually requires techniques distinct from those applied to typical earthquakes. Major advances in detection and understanding of tremor have derived in the past from a powerful combination of new data and new analysis techniques. In a recent study, Sagae et al. (2025, https://doi.org/10.1029/2025jb031348 ) exploit that combination again, developing a new machine‐learning based workflow and applying it to the S‐net cabled seismic network in the Japan trench offshore northern Honshu. Their approach, although complex, succeeds in detecting several times more tremor activity than earlier studies, resulting in new insights and providing a blueprint for similar approaches that could be applied elsewhere. As real‐time earthquake monitoring adopts similar tools, it may present an opportunity to bring tremor monitoring into operational workflows. In turn, this could solidify tremor monitoring as a component of future operational earthquake forecasting.