The messy truth of your AI strategies
Ryan welcomes Hema Raghavan, co-founder and head of engineering at Kumo.ai, to dive into all the messy stuff that comes with implementing AI, from pipeline sprawl to shadow AI. They discuss governance approaches like deploying models inside approved platforms and routing calls through monitored gateways, and how broken pipelines from complex feature-engineering motivated Kumo.ai’s approach of using a single foundation model with on-the-fly database queries.
Episode notes:
Kumo.ai allows you to train and run state-of-the-art AI models on your relational data, allowing you to make predictions about your users and transactions in seconds.
Connect with Hema on LinkedIn or reach out to her at her email hema@kumo.ai.
Congrats to user BalusC for winning a Populist badge on their answer to How to sanitize HTML code to prevent XSS attacks in Java or JSP?.
TRANSCRIPT
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Kumo’s Hema Raghavan: Turning Graph AI into ROI
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
Scaling Machine Learning on Graphs at LinkedIn with Hema Raghavan and Scott Meyer - TWiML Talk #236
Today we’re joined by Hema Raghavan and Scott Meyer of LinkedIn to discuss the graph database and machine learning systems that power LinkedIn features such as “People You May Know” and second-degree connections. Hema shares her insight into the motivations for LinkedIn’s use of graph-based models and some of the challenges surrounding using graphical models at LinkedIn’s scale, while Scott details his work on the software used at the company to support its biggest graph databases.