Today we’re joined by Hema Raghavan and Scott Meyer of LinkedIn.
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Hema is an Engineering Director Responsible for AI for Growth and Notifications, while Scott serves as a Principal Software Engineer. In this conversation, Hema, Scott and I dig into 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.
Thanks to our Sponsor!
I’d like to send a huge thanks to LinkedIn for sponsoring today’s show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of almost every product the company builds for its members and customers. LinkedIn’s highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.
Mentioned in the Interview
- Engineering at LinkedIn
- Problem Formulation for Machine Learning with Romer Rosales
- Productive Machine Learning at LinkedIn with Bee-Chung Chen
- Holistic Optimization of the LinkedIn Newsfeed with Tim Jurka
- GraphSAGE: Inductive Representation Learning on Large Graphs
- ClueWeb12 Dataset
- Check out all of our great series from 2018 at the TWiML Presents: Series page!
- TWiML Online Meetup
- Register for the TWiML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0