→ Automate cluster lifecycle management to reduce manual intervention. By triggering cluster creations, updates, and restarts based on operational demands, this automation ensures optimal resource allocation without manual oversight. → Streamline job orchestration and execution monitoring. Automatically create, start, cancel, and update job runs, enabling consistent execution of data pipelines and analytics workflows across teams. → Facilitate model registry governance and lifecycle control. Automate creation, versioning, approval, rejection, and tagging of ML models to maintain model quality and compliance throughout deployment.