Chanin Nantasenamat

In this hands-on lab, we put you in the driver’s seat of the modern AI application stack. Together we will build a powerful, data-friendly Python application using just two core technologies: Streamlit’s “pure Python" approach for a rapid, beautiful frontend, and Postgres for robust data handling and semantic search. We will build a functional AI agent that allows users to query data using Snowflake Cortex AI. You will learn how to build a Streamlit interface, connect to an agent, and add interactive, data-driven elements like charts, graphs and maps. You’ll leave this session with the tools to unlock value by turning existing data and static text into an interactive AI agent.

Moving ML from experimentation to production shouldn't require rebuilding from scratch or navigating lengthy development cycles. In this hands-on session, you'll build an end-to-end fraud detection model using an agentic approach in Snowflake. Starting with natural language prompts in Snowflake CoCo from Snowflake Notebooks, you'll train a model, register it to the Snowflake Model Registry and deploy it for real-time inference, all without leaving Snowflake. Walk away with practical techniques for creating production-ready prediction endpoints and scaling online inference workloads for fraud detection and beyond.