Distributed Readings

Training and evaluating AI models is resource-intensive, requiring significant human effort to generate quality training data and assess model outputs.

Advancing AI research requires collaboration between industry and academia, but funding and partnership models need structured programs.

Data science teams need diverse skill sets that blend mathematical rigor with creative problem-solving to build effective ML systems.

LinkedIn's Feed needed to evolve to handle increasing content diversity, real-time ranking signals, and personalization at massive scale.

LinkedIn's LLM-based ranking systems faced latency and throughput challenges when serving personalized results at scale.

Building personalized generative AI features at LinkedIn's scale required a robust and reliable application infrastructure that could serve millions of users.