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 Foundational ML Platforms with Kubernetes and Kubeflow with Ali Rodell - #595
AI-Powered Peer Programming with Vasi Philomin - #594
The Top 10 Reasons to Register for TWIMLcon: AI Platforms 2022!
Applied AI/ML Research at PayPal with Vidyut Naware - #593
Assessing Data Quality at Shopify with Wendy Foster - #592
Transformers for Tabular Data at Capital One with Bayan Bruss - #591
Understanding Collective Insect Communication with ML, w/ Orit Peleg - #590
Multimodal, Multi-Lingual NLP at Hugging Face with John Bohannon and Douwe Kiela - #589
Synthetic Data Generation for Robotics with Bill Vass - #588
Multi-Device, Multi-Use-Case Optimization with Jeff Gehlhaar - #587
Causal Conceptions of Fairness and their Consequences with Sharad Goel - #586
Brain-Inspired Hardware and Algorithm Co-Design with Melika Payvand - #585
Equivariant Priors for Compressed Sensing with Arash Behboodi - #584
Managing Data Labeling Ops for Success with Audrey Smith - #583
Engineering an ML-Powered Developer-First Search Engine with Richard Socher - #582
On The Path Towards Robot Vision with Aljosa Osep - #581
More Language, Less Labeling with Kate Saenko - #580
Optical Flow Estimation, Panoptic Segmentation, and Vision Transformers with Fatih Porikli - #579
Data Governance for Data Science with Adam Wood - #578
Feature Platforms for Data-Centric AI with Mike Del Balso - #577
Create your
podcast in
minutes
It is Free
20/20
The Dropout
Ten Percent Happier with Dan Harris
World News Tonight with David Muir
NEJM This Week