Lead Enterprise Data Architect, CYTEL, Richmond, Texas, USA

Title of the Talk :
AI Meets SQL: Democratizing Machine Learning with Databricks for Scalable Asset Intelligence

Abstract of the Talk:
Modern asset-intensive enterprises are increasingly turning to AI and machine learning (ML) to enhance operational efficiency, reduce downtime, and drive predictive decision-making. Yet, for many organizations, the barrier to entry remains high—requiring specialized skills, complex infrastructure, and significant overhead.

This session explores how the Databricks Data Intelligence Platform bridges that gap by enabling SQL-native development of ML models, making advanced analytics accessible to both data engineers and domain experts. Attendees will learn how AI and SQL can coexist to unlock scalable asset intelligence—without rewriting pipelines or retraining entire teams.

We’ll walk through key strategies for implementing ML workflows directly within the Databricks SQL environment, using tools like AutoML, feature stores, and Delta Live Tables. Through real-world use cases—such as predictive maintenance, equipment failure forecasting, and energy optimization—we’ll demonstrate how this approach reduces technical debt and accelerates time-to-value.

Whether you’re a data scientist, asset engineer, or digital transformation leader, this session will offer actionable insights into building robust ML solutions using familiar SQL syntax, all while harnessing the performance, governance, and scalability of the Databricks Lakehouse architecture.

Join us to discover how AI-powered asset management can be democratized through the convergence of SQL simplicity and ML sophistication.