Today we’re joined by Tim Jurka, Head of Feed AI at LinkedIn.
As you can imagine Feed AI is responsible for curating all the content you see daily on the LinkedIn site. What’s less apparent to those that don’t work on this type of product is the wide variety of opposing factors that need to be considered in organizing the feed. As you’ll learn in our conversation, Tim calls this the holistic optimization of the feed and we discuss some of the interesting technical and business challenges associated with trying to do this. We talk through some of the specific techniques used at LinkedIn like Multi-arm Bandits and Content Embeddings, and also jump into a really interesting discussion about organizing for machine learning at scale.
We’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 https://engineering.linkedin.com/blog.
The complete show notes can be found at https://twimlai.com/talk/224.
Building, Adopting, and Maturing LinkedIn's Machine Learning Platform with Ya Xu - #453
Expressive Deep Learning with Magenta DDSP w/ Jesse Engel - #452
Semantic Folding for Natural Language Understanding with Francisco Weber - #451
The Future of Autonomous Systems with Gurdeep Pall - #450
AI for Ecology and Ecosystem Preservation with Bryan Carstens - #449
Off-Line, Off-Policy RL for Real-World Decision Making at Facebook - #448
A Future of Work for the Invisible Workers in A.I. with Saiph Savage - #447
Trends in Graph Machine Learning with Michael Bronstein - #446
Trends in Natural Language Processing with Sameer Singh - #445
Trends in Computer Vision with Pavan Turaga - #444
Trends in Reinforcement Learning with Pablo Samuel Castro - #443
MOReL: Model-Based Offline Reinforcement Learning with Aravind Rajeswaran - #442
Machine Learning as a Software Engineering Enterprise with Charles Isbell - #441
Natural Graph Networks with Taco Cohen - #440
Productionizing Time-Series Workloads at Siemens Energy with Edgar Bahilo Rodriguez - #439
ML Feature Store at Intuit with Srivathsan Canchi - #438
re:Invent Roundup 2020 with Swami Sivasubramanian - #437
Predictive Disease Risk Modeling at 23andMe with Subarna Sinha - #436
Scaling Video AI at RTL with Daan Odijk - #435
Benchmarking ML with MLCommons w/ Peter Mattson - #434
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