→ Automate cluster lifecycle management by triggering updates and configuration changes directly through the API, ensuring clusters remain optimized without manual intervention.  → Streamline experiment tracking and management by automatically creating, updating, and deleting experiment runs and logged models, enhancing reproducibility and data science workflows.  → Manage and secure data pipelines and model registries via automated creation, permission updates, and lifecycle transitions, simplifying governance and compliance tracking.