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Kumo’s Hema Raghavan: Turning Graph AI into ROI
Jan 21, 2025
Hema Raghavan, co-founder and head of engineering at Kumo AI, previously led AI initiatives at LinkedIn, including the iconic 'People You May Know' feature. In this conversation, she unveils how graph neural networks (GNNs) revolutionize automated machine learning by transitioning from CPU to GPU. Hema explains how GNNs enhance recommendation systems, simplifying feature engineering and adapting to user preferences. She also discusses the transformation of relational data into graph formats and the critical role of explainable AI in advancing technology.
52:06
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Quick takeaways
- Kumo's integration of graph neural networks with existing data warehouses allows enterprises to automate machine learning efficiently, enhancing predictive capabilities without extensive manual input.
- The innovative query language developed by Kumo simplifies the process of defining machine learning problems, making advanced AI accessible to non-experts while empowering data scientists.
Deep dives
Innovative Approach to AutoML with Graph Neural Networks
Kumo AI focuses on automating machine learning (AutoML) specifically on GPUs, setting it apart from traditional AutoML solutions that rely on CPU-based models. This shift allows for building sophisticated AI models more efficiently, leveraging advanced graph neural networks (GNNs) to learn features automatically, without extensive manual feature engineering. As a result, enterprises can utilize their existing data warehouses—such as Snowflake and Databricks—to deploy predictive models rapidly and effectively. This innovation reduces the dependency on data science expertise while maximizing business value.
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