Petrobras MLOps Transformation With MLflow and Databricks

Discover how global energy leader Petrobras revolutionized their machine learning operations by transforming manual, error-prone model deployment processes into automated, scalable MLOps workflows. Learn how the company leveraged MLflow, Databricks Asset Bundles (DABs), and Unity Catalog to replace manual validation with automated metric-driven workflows, reducing model deployment timelines from days to hours while establishing granular governance and reproducibility across production models. Explore the specific strategies and technical implementations that enabled Petrobras data scientists to focus on innovation rather than infrastructure through standardized pipelines, while ensuring compliance and scalability in one of the world's most complex energy ecosystems. Gain insights into overcoming MLOps bottlenecks that delay critical business insights and understand how to implement automated validation processes that maintain quality while accelerating deployment cycles. The session demonstrates practical approaches to building robust MLOps frameworks that can handle enterprise-scale machine learning operations while maintaining the governance and reliability requirements of mission-critical energy industry applications.