Inference Graphs at LinkedIn Using Ray-Serve

LinkedIn's AI platform is undergoing a transformation with the help of Ray and Inference Graphs, simplifying complex AI workflows and enabling better integration, resource utilization, and seamless transitions between online and offline inference. The company faced challenges such as managing complex AI workflows, integrating multiple programming languages, and adapting to an ever-evolving AI landscape. LinkedIn's solution involves adopting Ray and the concept of Inference Graphs, which simplify AI workflows, allow for heterogeneous infrastructure, and enable seamless transition between offline and online inference. The platform has seen significant changes in handling AI workloads, with minimal overhead for LLN use cases and optimized serialization and deserialization costs for personalization models. LinkedIn is actively exploring further optimizations to maximize the efficiency of AI workloads.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.