ml-pipeline-setup

The skill described presents a coherent, end-to-end ML pipeline orchestration pattern for Databricks, aligning with its stated purpose of enabling ML pipelines, training with Feature Store, and batch inference with Unity Catalog integration. It relies on official Databricks components (MLflow, UC, Feature Store, Asset Bundles) and upstream patterns from a known repository, with no evident use of unverifiable binaries or external data exfiltration channels. The credential surface appears limited to workspace and UC registries via Databricks authentication, which is standard for this domain. Overall, the footprint is proportionate and appropriate for the stated ML orchestration purpose, with benign security posture given the described data flows. Monitor for any future inclusion of external scripts or third-party binaries in Asset Bundles to ensure continued alignment with secure supply-chain practices.