AI Seminar (Virtual): GraphHouse and Training GNNs at Internet Scale

Jul 25, 12:00 PM PDT. Add to calendar: Google | Yahoo
  • Virtual () SF Big Analytics
  • 58 RSVP

This virtual AI seminar is hosted by SF Big Analytics.

Tech Talk: A Cloud Native Graph Database Designed For Datalake
Speaker: Mingxi Wu (TigerGraph)
Abstract: In this talk, we unveil TigerGraph new cloud-native product powered by the cutting-edge GraphHouse architecture, a fusion of Graph analytics and data lakes. GraphHouse enables seamless data ingress from Delta Lake, Iceberg, Snowflake, Postgres, BigQuery, etc., utilizing vertices and edges to store data directly on cost-effective, reliable cloud object storage. By decoupling the compute layer from the data layer, users can dynamically spin up compute clusters on-demand, accessing a single connected dataset. All graph database capabilities, including data silo integration, fast multi-hop traversals, and query visualizations, are seamlessly inherited, with unlimited compute capacity.

Tech Talk: Training GNNs at Internet Scale using cuGraph and WholeGraph
Speaker: Joe Eaton (NVIDIA)
Abstract: Joe Eaton is a Distinguished System Engineer for Data and Graph Analytics at NVIDIA, and is currently leading the company strategy for Graph Neural Networks at Nvidia. Joe leads teams for code optimization, graph analysis, framework development and optimization, as well as interacts with prospective customers in industry. Key areas of interest are financial services, retail Recommenders, and molecular generation for drug discovery.

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Mingxi Wu (TigerGraph), Joe Eaton (NVIDIA)

Zvonko is a Principal Systems Engineer at NVIDIA, working on the Cloud Native Technologies team. Focusing right now on all things related to confidential computing, especially in the context of accelerators.
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