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Graph Learning at the Scale of Modern Data Warehouses
Data warehouses have become a staple for enterprises, providing a wealth of information that can be harnessed to improve decision-making through the use of machine learning (ML). The data stored in these warehouses is typically relational and can be viewed as a graph of entities and relationships. Graph learning with graph neural networks (GNNs) offers a natural and effective way to apply ML to this type of data. However, deploying GNNs at scale presents several challenges, including transforming the data for graph learning, scaling the graph learning framework, and performing predictions in a reasonable amount of time. This presentation will outline our comprehensive approach to addressing these challenges and show how we built an efficient and scalable end-to-end system for graph learning in data warehouses.
Founding Engineer @Kumo.ai
Subramanya Dulloor is a computer scientist and engineer with extensive experience in distributed systems and systems for ML. As a founding engineer at Kumo.ai, he is focused on making predictive analytics on data warehouses plug-and-play easy. Before joining Kumo, Dulloor worked on distributed systems and database internals at various startups. He also spent several years at Intel Labs conducting research in memory management in operating systems and VMMs, as well as in distributed high-performance systems for machine learning and computer vision.
Dulloor holds a PhD in computer science from Georgia Tech and has over a dozen publications in top-tier conferences and patents to his name.
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