You are viewing content from a past/completed conference.
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.
Speaker
Subramanya Dulloor
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.
Read more
Find
Subramanya Dulloor
at:
From the same track
Session
Machine Learning
Strategy & Principles to Scale and Evolve MLOps @DoorDash
Wednesday Mar 29 / 10:35AM BST
MLOps has become a major enabler to successfully operationalize ML applications and for ML practitioners to realize the power of ML to bring impact to business. The journey to implementing MLOps will be unique to each company.
Hien Luu
Sr. Engineering Manager @Zoox & Author of MLOps with Ray, Speaker and Conference Committee Chair
Strategy & Principles to Scale and Evolve MLOps @DoorDash
Session
AI
Responsible AI: From Principle to Practice!
Wednesday Mar 29 / 01:40PM BST
Enabling responsible development of artificial intelligent technologies is one of the major challenges we face as the field moves from research to practice. Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learn
Mehrnoosh Sameki
Principal PM Manager @Microsoft
Responsible AI: From Principle to Practice!
Session
Digital Twins
Cognitive Digital Twins: A New Era of Intelligent Automation
Wednesday Mar 29 / 11:50AM BST
Traditionally, Digital Twins have been helping businesses make data-driven decisions, increase efficiency, and improve the overall performance of their physical assets.
Yannis Georgas
Intelligent Industry Lead @Capgemini
Cognitive Digital Twins: A New Era of Intelligent Automation
Session
python
Simplifying Real-Time ML Pipelines with Quix Streams: An Open Source Python Library for ML Engineers
Wednesday Mar 29 / 04:10PM BST
As data volume and velocity continue to increase, the need for real-time machine learning (ML) is becoming more pressing. However, building real-time ML pipelines can be complex and time-consuming, requiring expertise in both ML and streaming application development.
Tomáš Neubauer
CTO & Co-Founder @Quix
Simplifying Real-Time ML Pipelines with Quix Streams: An Open Source Python Library for ML Engineers