Why most Databricks AI work stalls
The platform is rarely the problem. The platform configuration is rarely the problem either. What stalls Databricks-led AI delivery is everything around the model: data foundations, integration points, governance, cost discipline, and operational ownership.
What a production-grade delivery actually includes
- A model-ready data product, not a brittle notebook query.
- A deployment pipeline with versioning, monitoring, and rollback.
- A governance posture that survives audit and compliance review.
- An operational owner who can be paged when something breaks.
The playbook in one paragraph
Treat the model as the smallest part of the system. Spend most of the delivery effort on the data product, the integration into the workflow, the monitoring, and the governance. Then the model itself becomes the easy part, and the work holds up after handover.