The correct approach is to use MLflow for packaging the model and deploying it as a REST API endpoint. MLflow simplifies the deployment process by encapsulating the model, its dependencies, and necessary configurations, making it easier to manage in a production environment. This method supports real-time predictions through HTTP requests. Other options like exporting the model as a serialized file (Option C) or saving it to a Delta table (Option A) are less scalable or not suited for real-time predictions. Scheduling periodic runs (Option B) is more appropriate for batch processing rather than on-demand predictions.