Episode

Kumo AI & Relational Deep Learning | Data Brew | Episode 34

Podcast
Data Brew by Databricks
Published
Oct 14, 2024
Duration seconds
2607
Processing state
processed
Canonical source
https://www.buzzsprout.com/1370119/episodes/15897509-kumo-ai-relational-deep-learning-data-brew-episode-34.mp3
Audio
https://www.buzzsprout.com/1370119/episodes/15897509-kumo-ai-relational-deep-learning-data-brew-episode-34.mp3
JSON
/v1/public/podcasts/data-brew-by-databricks/episodes/kumo-ai-relational-deep-learning-data-brew-episode-34
Markdown
/podcast/data-brew-by-databricks/kumo-ai-relational-deep-learning-data-brew-episode-34.md

Actions

  • POST https://stenobird.com/v1/public/podcasts/data-brew-by-databricks/episodes/kumo-ai-relational-deep-learning-data-brew-episode-34/transcription-requests
    Idempotently request low-priority transcript generation for this episode.
  • GET https://stenobird.com/podcast/data-brew-by-databricks/kumo-ai-relational-deep-learning-data-brew-episode-34.md
    Read the agent-friendly Markdown representation of this episode resource.

Summary

In this episode, Jure Leskovec, Co-founder of Kumo AI and Professor of Computer Science at Stanford University, discusses Relational Deep Learning (RDL) and its role in automating feature engineering. Highlights include: - How RDL enhances predictive modeling. - Applications in fraud detection and recommendation systems. - The use of graph neural networks to simplify complex data structures.