LinkedIn, a platform with over 1.3 billion users, recently overhauled its feed retrieval system, replacing five separate pipelines with a single Large Language Model (LLM). This transition aimed to enhance the platform’s understanding of professional context while optimizing operational costs at scale.
The redesign impacted three key areas: content retrieval, ranking, and compute management. LinkedIn’s Vice President of Engineering, Tim Jurka, highlighted the significant infrastructure reinvention achieved through this transition.
One of the primary challenges faced by LinkedIn was matching users’ professional interests with their actual behavior and surfacing diverse content beyond their immediate network. By unifying the feed retrieval pipelines, LinkedIn sought to provide a more personalized and relevant experience to its members.
The company’s shift to LLMs necessitated updates to the surrounding architecture, streamlining member context maintenance and data sampling processes. Additionally, LinkedIn introduced a prompt library to convert data into text for LLM processing, enhancing the model’s ability to interpret engagement signals accurately.
Furthermore, LinkedIn reimagined its post ranking approach, leveraging a Generative Recommender model that considers historical interactions as a professional journey, ensuring more tailored content delivery.
To address the computational challenges posed by running LLMs at LinkedIn’s scale, the company optimized its training infrastructure, disaggregated CPU-bound and GPU-heavy tasks, and parallelized checkpointing processes to maximize GPU utilization.
LinkedIn’s journey in modernizing its feed retrieval system offers valuable insights for tech enthusiasts and engineers, showcasing the complexities involved in deploying advanced models at scale and the importance of thoughtful infrastructure design.
Source: VentureBeat