Every Databricks optimization platform can tell you what should change. The harder question is when a system should be trusted to make that change on its own.
This session follows an Unravel customer's journey from surfacing Databricks optimization recommendations to safely applying them in production. We cover the engineering decisions, the guardrails, and the trust model that made autonomous optimization possible where every change carries operational risk.
Walk away with a practical framework for building systems that earn the right to act autonomously, plus the lessons and results from a large-scale enterprise Databricks deployment. Watch now.
In this 30-minute session plus live Q&A, Head of Artificial Intelligence Prajakta Kalmegh will guide you through:
- Why recommendations are only the beginning: Learn why there is a gap between identifying an optimization opportunity and making the change in production.
- What makes a recommendation actionable: Discover the technical and operational checks that determine whether a recommendation is ready to execute.
- Building trust, one step at a time: See how to move from manual approvals to scoped autonomy instead of jumping straight to full automation.
- Auto-Apply in production: Learn how recommendations are validated, executed, and continuously verified to deliver the expected outcome.
- Keeping humans in the loop: See how a PR-based workflow keeps every automated decision transparent, reviewable, and auditable.
