Why it matters: In banking, churn must be identified at scale across multiple products, under strict regulatory constraints, using interpretable models despite severe class imbalance. What we cover: how an ML factory standardizes and accelerates churn deployment while ensuring explainability and governance. Takeaways: how banks can reduce time‑to‑production and deliver controlled, measurable business impact with ML.