Amplify Your Machine Learning Workflows Webinar

Embed your company’s data to power any growth ML use case in production

What is it?

This webinar will cover what every data scientist, applied machine learning engineer, or research scientist needs to know in order to leverage best-in-class graph machine learning on their data.

You’ll see a deep dive from the core team and industry leaders on how to effectively build, manage, and scale your graph to production use cases. You’ll learn how to quickly operationalize Graph Neural Networks into existing workflows and generate highly optimized and custom embeddings for your use case. You can read more about the embeddings here.

Whether you’re just starting out or are already building and deploying graph learning in production, this workshop is designed to accelerate your journey into operationalized graph learning to make your life easier.

When is it?

Tuesday March 7, 2023 - 10 AM PST

What will we cover?

What is Kumo and how does the platform support your journey in graph learning?
How Kumo builds, manages, and scales your graph so you can focus on the core problems?
How you can answer any questions on your graph using Predictive Query?
How you can generate embeddings from your graph and use them in downstream ML applications?
How Kumo can operationalize GNNs with production-ready workflows and orchestration so you can be production-ready instantly?

How do I sign up?

Fill out our registration form

Speakers

Matthias Fey

Matthias Fey is a founding engineer at Kumo.ai, working on making state-of-the-art Graph Neural Network solutions readily available to large-scale data warehouses. Previously, he obtained his PhD at the computer graphics lab at the TU Dortmund University, Germany. His main area of research lies in the development of new deep learning methods that can be directly applied to unstructured data such as graphs, point clouds and manifolds. Furthermore, he is the creator of the PyTorch Geometric library. and is a core member on the Open Graph Benchmark (OGB) team.

Manan Shah

Manan Shah is a founding engineer at Kumo AI, where he spends his time building and scaling graph neural networks to support predictive queries on large scale data warehouses. He has a BS and MS in computer science from Stanford University with a focus on theory, machine learning, and systems, and has previously spent time working on problems in machine intelligence at Google AI, Snorkel AI, and Deep Valley Labs. He is also an active contributor to the Pytorch Geometric (PyG) library.

Ivaylo Bahtchevanov

Ivaylo is the community lead for PyG and product team at Kumo. Ivaylo has experience leading product orgs at UiPath. He was the head of AI at the identity software leader ForgeRock, running data science and applied ML. Before that, he led data science teams at smaller companies (Stella.ai), and prior to that, developed ML applications for the Department of Defense (including work on predictive analytics on ballistic missile systems, mapping battlefields, and autonomous systems). Before that he got his Bachelor's and Master's at Stanford in computer science, focused in AI.