VK has shortened the launch of a new recommendation vertical from weeks to one day The VK AI team explained what has changed in the discovery platform in recent months. This is one layer of infrastructure for recommendations, search and advertising in all the company's products. About 90% of ranking problems there are solved by configuration, without new code. So what we did: - Quick start. The vertical is described in the configuration (object catalog, candidate sources, profile, optimization targets), and the platform itself deploys services, data flows, monitoring and A/B cycle. This routine used to last for weeks. This created 15 new branches, including for small scenarios like comment ranking. —. Discovery portal. One point for configuration management, service topology and monitoring. The changes are validated before implementation, published step by step with comparison of metrics, and rolled back in case of withdrawal. Returning to the previous version is done in one step. All AI VK developers use the portal; Dozens of changes go through it every day. —. GPU rating. In a circuit with millisecond delays, the team introduced dynamic batching, latency tail control, and a fallback path to a simple model in case of GPU failure. The neural network model is now included in the configuration (and not as a separate project for the quarter, as is usually the case). - Saving iron. Moving some of the calculations to the GPU, reusing features between products, caching candidates, and optimizing the hot query path freed up 70k CPU cores. In some products the effect reached 30%. After implementing all changes in recommendations, the time in the VKontakte feed increased by 11%, and the addition of friends from recommendations increased by almost 2.7 times. In VK Clips, viewing time increased by 22%. In the main VK video, the viewing time increased by 20%. In advertising, after switching to the platform, the conversion of an impression to a target action for messages to the community increased by 46% and the price of a targeted action decreased by 13%.

VK has shortened the launch of a new recommendation vertical from weeks to one day The VK AI team explained what has changed in the discovery platform in recent months. This is one layer of infrastructure for recommendations, search and advertising in all the company's products. About 90% of ranking problems there are solved by configuration, without new code. So what we did: - Quick start. The vertical is described in the configuration (object catalog, candidate sources, profile, optimization targets), and the platform itself deploys services, data flows, monitoring and A/B cycle. This routine used to last for weeks. This created 15 new branches, including for small scenarios like comment ranking. —. Discovery portal. One point for configuration management, service topology and monitoring. The changes are validated before implementation, published step by step with comparison of metrics, and rolled back in case of withdrawal. Returning to the previous version is done in one step. All AI VK developers use the portal; Dozens of changes go through it every day. —. GPU rating. In a circuit with millisecond delays, the team introduced dynamic batching, latency tail control, and a fallback path to a simple model in case of GPU failure. The neural network model is now included in the configuration (and not as a separate project for the quarter, as is usually the case). - Saving iron. Moving some of the calculations to the GPU, reusing features between products, caching candidates, and optimizing the hot query path freed up 70k CPU cores. In some products the effect reached 30%. After implementing all changes in recommendations, the time in the VKontakte feed increased by 11%, and the addition of friends from recommendations increased by almost 2.7 times. In VK Clips, viewing time increased by 22%. In the main VK video, the viewing time increased by 20%. In advertising, after switching to the platform, the conversion of an impression to a target action for messages to the community increased by 46% and the price of a targeted action decreased by 13%.