How does R manage large-scale feature engineering pipelines in production ML systems?

Feature engineering in R is managed using DAG pipelines, caching, and distributed backends like Spark or targets.

Production ML systems separate feature computation from model training. R pipelines often use targets or sparklyr to compute features incrementally, cache them, and ensure reproducibility. Feature stores externalize computation results for reuse across models.