Felix GV, a principal engineer at LinkedIn, discusses Venice, a derived database developed in-house and open-sourced in 2022. Designed for AI feature data storage, Venice handles data generated from existing datasets, facilitating global-scale AI inference workloads. The system's growth reflects the increasing need for efficient data management solutions, particularly in the realm of recommender systems and other AI-driven applications. Felix emphasizes the distinction between primary and derived data storage solutions, positioning Venice as a solution suited for specific data storage needs in tech environments.
"Venice is the system that I worked on, a derived database that focuses on storing AI feature data sets, enabling efficient AI inference workloads."
"The scope and scale of Venice is continuously growing, reflecting the increasing demands for efficient data handling in AI applications."
Collection
[
|
...
]