A behind-the-scenes look at how large organizations move from AI experimentation to real impact. The session explores how to structure innovation, prioritize use cases, and turn AI initiatives into measurable business value.
A real-world case study on fraud detection combining classical ML, GenAI, and graph methods. It shows how structured AI development helps organizations continuously adapt to evolving threats.
AI is reshaping how products are built and how teams operate. This talk explores the convergence of product, design, data, and engineering in AI-native organizations.
How Netflix evolved its data platform from legacy warehouse systems to a unified Iceberg-based lakehouse at massive scale.
How to build production-ready AI agents that combine autonomy with strict business rules using LangGraph, guardrails, and deterministic tool usage.
A framework for evaluating AI agents based on real business impact, helping teams decide where to invest, optimize, or stop.
A practical look at real-time recommendation systems using contextual bandits in large-scale environments like mobile gaming.
How AI is transforming data engineering through coding agents, MCP integrations, and semantic layers enabling both humans and agents to access data.
A practical overview of managing schemas, lineage, and governance in Kafka-based streaming ecosystems.
Why structured knowledge and ontologies are critical for enabling AI systems to understand complex financial concepts.
How large-scale ML pipelines process satellite data to support climate monitoring and carbon credit systems.
An introduction to a new generation of time-series models and how they simplify forecasting across domains.
A practical session on identifying risks in AI systems, including vulnerabilities, prompt-based leaks, and agent security.
A deep dive into Graph RAG and knowledge graphs, enabling more advanced reasoning and context-aware AI systems.
Hands-on workshop using open geospatial data for ML use cases like pricing and urban planning.
Build a working MCP server and understand how agents connect to real-world tools in production environments.
A live session showing how to build an end-to-end data product using GenAI and Databricks AI Dev Kit 5–10× faster.