It helps data scientists and engineers define testable, modular, self-documenting dataflows that encode lineage and metadata. The framework runs anywhere Python runs and is designed to scale. While not an ML model itself, it is explicitly used in llmops and rag contexts as an orchestration layer for building AI data pipelines and feature engineering workflows.