Mohit Saxena

Running analytics workloads at scale carries a hidden operational tax: teams commonly lose significant engineering time to debugging failures, leave performance and cost improvements on the table, and spend months on each version upgrade.

Building a production-ready AI strategy requires more than just a model; it requires a unified foundation that handles massive scale without sacrificing governance. In this session, we’ll deep dive into the technical framework powering the next generation of data intelligence.We’ll explore how to build a unified platform that bridges the gap between massive-scale data engineering and downstream AI-driven insights. We'll focus on: 1. How to optimize TCO using Google Cloud’s auto-scaling compute for Databricks workloads. 2. How the Knowledge Catalog and Unity Catalog federation enables a "single pane of glass" for governance, allowing teams to query sensitive records across platforms without data movement. 3. Leveraging Vertex AI and Databricks to move from raw data to intelligent document processing and risk assessment. 4. Why an open-source foundation is the key to avoiding vendor lock-in and maintaining multi-cloud flexibility.

Discover how the AI-driven capabilities of Amazon Q Developer streamline data integration across AWS services, such as AWS Glue, Amazon SageMaker Catalog, Amazon Redshift, Amazon SageMaker AI, and more.