This presentation explores how Databricks' Data Intelligence Platform supports the development and deployment of responsible AI in credit decisioning, ensuring fairness, transparency and regulatory compliance. Key areas include bias and fairness monitoring using Lakehouse Monitoring to track demographic metrics and automated alerts for fairness thresholds. Transparency and explainability are enhanced through the Mosaic AI Agent Framework, SHAP values and LIME for feature importance auditing. Regulatory alignment is achieved via Unity Catalog for data lineage and AIBI dashboards for compliance monitoring. Additionally, LLM reliability and security are ensured through AI guardrails and synthetic datasets to validate model outputs and prevent discriminatory patterns. The platform integrates real-time SME and user feedback via Databricks Apps and AI/BI Genie Space.

In this session, we will explore effective strategies for optimizing costs on the Databricks platform, a leading solution for handling large-scale data workloads. Databricks, known for its open and unified approach, offers several tools and methodologies to ensure users can maximize their return on investment (ROI) while managing expenses efficiently. Key points: Understanding usage with AI/BI tools Organizing costs with tagging Setting up budgets Leveraging System Tables By the end of this session, you will have a comprehensive understanding of how to leverage Databricks' built-in tools for cost optimization, ensuring that their data and AI projects not only deliver value but do so in a cost-effective manner. This session is ideal for data engineers, financial analysts, and decision-makers looking to enhance their organization’s efficiency and financial performance through strategic cost management on Databricks.