Netflix's Analytics Summit showcases how analytics informs business decisions, including content launch risk. Content goes through development, production, post-production, and launch preparation, with launch preparation relying on finalized media assets. Teams face a trade-off between waiting for the final IMF, risking delays, or starting early with a less final Locked Cut, risking rework. Manually provided production schedules for these assets often lack accuracy and coverage, especially far from delivery. This inaccuracy is strongly correlated with content launch misses. To address this, Netflix developed predictive models to estimate media asset delivery dates. These boosted tree regression models use production-level signals, metadata, and seasonal data, updated daily. The models aim to fill schedule gaps and improve existing date accuracy. Evaluating these models involves metrics like mean absolute error, bias, and error distribution. Backtests show significant improvements in predictive accuracy compared to manual schedules, reducing forecast errors. These improved predictions lead to lower Accumulated Error Days, a metric tied to launch misses. The predictive dates integrate into existing workflows, offering earlier accuracy than scheduled dates. Serving logic defaults to scheduled dates where the model underperforms, while other teams can view both and use their judgment. This initiative streamlines launch workflows and mitigates launch risks by providing more reliable delivery estimates.