Abstract: Embodiments of the disclosed technologies receive a first signal from a user session and create a first ranked list of content items. Based on the first signal, a first subset of the first ranked list is assigned to a first set of slots of the user session. A second subset of the first ranked list is assigned to a second set of slots of the user session. Based on user activity data and position context data, a second ranked list of content items is created. The second ranked list is assigned instead of the second subset to the second plurality of slots.

Abstract: Embodiments of the disclosed technologies receive a first signal from a user session and create a first ranked list of content items. Based on the first signal, a first subset of the first ranked list is assigned to a first set of slots of the user session. A second subset of the first ranked list is assigned to a second set of slots of the user session. Based on user activity data and position context data, a second ranked list of content items is created. The second ranked list is assigned instead of the second subset to the second plurality of slots.

Abstract: The disclosed embodiments provide a system for testing machine learning workflows. During operation, the system obtains a configuration for a staging test of a machine learning model, wherein the configuration includes a model name for the machine learning model, a duration of the staging test, and a use case associated with the machine learning model. Next, the system selects a staging test host for the staging test. The system then deploys the staging test on the staging test host in a staging environment, wherein the deployed staging test executes the machine learning model based on live traffic received from a production environment. After the staging test has completed, the system outputs a set of metrics representing a system impact of the machine learning model on the staging test host.

Abstract: Methods, systems, and computer programs are presented for reducing latency for providing a user feed containing one or more posts. One method includes operations for receiving a request to access the user feed and for performing a first query to search posts. The first query uses a first time horizon delimiting a creation time of posts and a first maximum number of posts selected for ranking. The posts from the first query are sent to the client device for presentation on a user interface. Further, a second query is performed to search posts, where the second query uses a second time horizon that is greater than the first time horizon and a second maximum number of posts for ranking that is greater than the first maximum number of posts. The posts from the first query and the second query are merged and sent to the client device for presentation.

Abstract: Methods, systems, and computer programs are presented for reducing latency for providing a user feed containing one or more posts. One method includes operations for receiving a request to access the user feed and for performing a first query to search posts. The first query uses a first time horizon delimiting a creation time of posts and a first maximum number of posts selected for ranking. The posts from the first query are sent to the client device for presentation on a user interface. Further, a second query is performed to search posts, where the second query uses a second time horizon that is greater than the first time horizon and a second maximum number of posts for ranking that is greater than the first maximum number of posts. The posts from the first query and the second query are merged and sent to the client device for presentation.

Abstract: The disclosed embodiments provide a system for testing machine learning workflows. During operation, the system obtains a configuration for a staging test of a machine learning model, wherein the configuration includes a model name for the machine learning model, a duration of the staging test, and a use case associated with the machine learning model. Next, the system selects a staging test host for the staging test. The system then deploys the staging test on the staging test host in a staging environment, wherein the deployed staging test executes the machine learning model based on live traffic received from a production environment. After the staging test has completed, the system outputs a set of metrics representing a system impact of the machine learning model on the staging test host.