As a global energy leader, Petrobras relies on machine learning to optimize operations, but manual model deployment and validation processes once created bottlenecks that delayed critical insights. In this session, we’ll reveal how we revolutionized our MLOps framework using MLflow, Databricks Asset Bundles (DABs) and Unity Catalog to: Replace error-prone manual validation with automated metric-driven workflows Reduce model deployment timelines from days to hours Establish granular governance and reproducibility across production models Discover how we enabled data scientists to focus on innovation—not infrastructure—through standardized pipelines while ensuring compliance and scalability in one of the world’s most complex energy ecosystems.