Abstract: Implementations described herein relate to methods, systems, and computer-readable media for automated generation and use of a machine learning (ML) model to provide recommendations. In some implementations, a method includes receiving a recommendation specification that includes a content type and an outcome identifier, and determining model parameters for a ML model based on the recommendation specification. The method further includes generating a historical user feature matrix (FM), generating a historical content feature matrix (FM), and transforming the historical user FM and the historical content FM into a suitable format for the ML model. The method further includes obtaining a target dataset that includes historical results for the outcome identifier for a plurality of pairs of user identifiers and content items of the content type. The method further includes training the ML model using supervised learning to generate a ranked list of content items for each user identifier.

Abstract: An index to be backed up in a computing environment is identified. A staged copy of the index to be backed up is created. The staged copy of the index is segmented. The segments are uploaded to a pre-selected location.

Abstract: An index to be backed up in a computing environment is identified. A staged copy of the index to be backed up is created. The staged copy of the index is segmented. The segments are uploaded to a pre-selected location.

Abstract: An index to be backed up in a multitenant environment is identified. The multitenant environment includes data for multiple client entities, each identified by a tenant identifier (ID) having one of one or more users associated with the tenant ID. Users of each of multiple client identities can only access data identified by a tenant ID associated with the respective client entity. The multitenant environment is a hosted database provided by an entity separate from the client entities, and provides on-demand database service to the client entities. A staged copy of the index to be backed up is created. The staged copy of the index is segmented. The segments are uploaded to a pre-selected location.

Abstract: Disclosed are methods, apparatus, systems, and computer readable storage media for finding people in a physical environment. A server receives data indicating a location and identifies events corresponding to the location. Event data is transmitted to a computing device. Attendees at a selected event are identified and transmitted to the computing device. The server can receive a message addressed to an attendee and store data regarding the attendee. In some implementations, attendees may be identified from content posted on a social network. Additionally, certain attendees or events may be emphasized.

Abstract: An index to be backed up in a multitenant environment is identified. The multitenant environment includes data for multiple client entities, each identified by a tenant identifier (ID) having one of one or more users associated with the tenant ID. Users of each of multiple client identities can only access data identified by a tenant ID associated with the respective client entity. The multitenant environment is a hosted database provided by an entity separate from the client entities, and provides on-demand database service to the client entities. A staged copy of the index to be backed up is created. The staged copy of the index is segmented. The segments are uploaded to a pre-selected location.