Hema Raghavan is co-founder of Kumo, a company that makes graph neural networks accessible to enterprises by connecting to their relational data stored in Snowflake and Databricks. Hema talks about how running GNNs on GPUs has led to breakthroughs in performance as well as the query language Kumo developed to help companies predict future data points. Although approachable for non-technical users, the product provides full control for data scientists who use Kumo to automate time-consuming feature engineering pipelines.
Mentioned in this episode:
Graph Neural Networks: Learning mechanism for data in graph format, the basis of the Kumo product
Graph RAG: Popular extension of retrieval-augmented generation using GNNs
LiGNN: Graph Neural Networks at LinkedIn paper
KDD: Knowledge Discovery and Data Mining Conference
Hosted by: Konstantine Buhler and Sonya Huang, Sequoia Capital
Kumo’s Hema Raghavan: Turning Graph AI into ROI | Training Data podcast - Listen or read transcript on Metacast
