A retailer needs a separate demand-forecasting model for each of its 2,000 stores. The training data is a 300-million-row Delta table with a store_id column, and any single store's history fits comfortably in the memory of one worker. The team wants to keep using scikit-learn, train the 2,000 models in parallel across the cluster's workers, and log each store's model and metrics to MLflow.