Abstract: Methods and systems for multi-model scoring in a multi-tenant system are presented. A request for a machine learning application is received from a tenant application. A tenant identifier that identifies one of the multiple tenants is determined. Based on the tenant identifier and a type of the machine learning application, a first and a second machine learning models are determined. The first machine learning model was generated based on a first training data set associated with the tenant identifier. The second machine learning model that was generated based on a second training data set associated with the tenant identifier. A flow of operations that includes running the first and second machine learning models with data related to the request is executed to obtain a scoring result. The scoring result is returned to the tenant application in response to the request.

Abstract: A method by a router component in a multi-tenant on-demand serving infrastructure to route scoring requests to scoring containers. The method includes receiving a scoring request, determining a machine learning application associated with the scoring request, determining whether a router instance for the machine learning application exists, and responsive to a determination that a router instance for the machine learning application does not exist, obtaining a configuration object for the machine learning application and instantiating the router instance for the machine learning application based on the configuration object for the machine learning application. The method further includes invoking the router instance for the machine learning application to route the scoring request associated with the machine learning application to a scoring container that provides scoring functionality for the machine learning application.

Abstract: A method by one or more electronic devices to provision an infrastructure for a machine learning application in a multi-tenant on-demand serving infrastructure. The method includes storing a plurality of templates, wherein each of the plurality of templates indicates a scoring interface, a web server, a definition of a continuous integration pipeline, and a definition of a continuous deployment pipeline, receiving a request to provision the infrastructure for the machine learning application using a specified template from the plurality of templates, and provisioning the infrastructure for the machine learning application using the specified template to create a version control system repository, a continuous integration pipeline, and a continuous deployment pipeline.

Abstract: A method by one or more electronic devices for spinning up a scoring container on demand. The method includes receiving, from an orchestrator component via an application programming interface (API), a request to spin up the scoring container, wherein the scoring container is configured to provide scoring functionality, spinning up the scoring container responsive to receiving the request to spin up the scoring container, and providing, to the orchestrator component via the API, a response to the request to spin up the scoring container, wherein the response includes a uniform resource locator (URL) to use to submit scoring requests to the scoring container.

Abstract: A method performs service discovery in a machine learning service. The method includes detecting initialization of at least one service container, identifying label information in the at least one service container, collecting the label information for the initializing at least one service container, and storing the label information in a routing information storage to enable routing of requests to the at least one service container.

Abstract: A method and system for a single framework for both streaming and on-demand inference that includes receiving a request from a tenant application for a machine-learning serving infrastructure, where the request identifies features of tenant data and a machine-learning model, subscribing to events for the identified features, initiating the machine-learning model for the request, and generating a prediction using the machine-learning model on the identified features.

Abstract: Methods and systems are described for creating a custom index in a multi-tenant database environment. In one embodiment, a method includes obtaining query for a multi-tenant database that is recommended as a candidate for creating an additional filter, evaluating the query against criteria to determine whether to select the query for creating the additional filter, and creating the additional filter for the query, if the query is selected.

Abstract: In an on demand database system, a query engine applies a custom index for inner queries. The query engine receives a query and determines that the query has an inner query nested within the primary query. The query engine identifies that a custom index exists for a client associated with the query, and applies the custom index to filter results for the query. The custom index includes a subset of information of a table that includes accounts for multiple different clients. By using the custom index, the query engine can filter the results more efficiently that if solely using the multiple client table.

Abstract: Methods and systems are described for creating a custom index in a multi-tenant database environment. In one embodiment, a method includes obtaining query for a multi-tenant database that is recommended as a candidate for creating an additional filter, evaluating the query against criteria to determine whether to select the query for creating the additional filter, and creating the additional filter for the query, if the query is selected.

Abstract: Statistics mechanisms in multitenant database environments. A master statistics file is maintained in a multitenant database system. The master statistics file has statistics corresponding to multiple tenants within the multitenant database system. Statistics for a selected table within the multitenant database system are generated. The selected table corresponding to a selected tenant of the multitenant database system. The master statistics file is updated based on the generated statistics for the selected table.

Abstract: Statistics mechanisms in multitenant database environments. A master statistics file is maintained in a multitenant database system. The master statistics file has statistics corresponding to multiple tenants within the multitenant database system. Statistics for a selected table within the multitenant database system are generated. The selected table corresponding to a selected tenant of the multitenant database system. The master statistics file is updated based on the generated statistics for the selected table.

Abstract: Statistics mechanisms in multitenant database environments. A master statistics file is maintained in a multitenant database system. The master statistics file has statistics corresponding to multiple tenants within the multitenant database system. Statistics for a selected table within the multitenant database system are generated. The selected table corresponding to a selected tenant of the multitenant database system. The master statistics file is updated based on the generated statistics for the selected table.

Abstract: Methods and systems are provided for querying a database. One exemplary method for obtaining data from an on-demand database supporting one or more tenants involves obtaining model database statistics based on expected utilization information for a tenant and providing the model database statistics to the on-demand database. The on-demand database utilizes the model database statistics to generate a query plan and executes the query plan to obtain data from the model database statistics.

Abstract: In an on demand database system, a query engine applies a custom index for inner queries. The query engine receives a query and determines that the query has an inner query nested within the primary query. The query engine identifies that a custom index exists for a client associated with the query, and applies the custom index to filter results for the query. The custom index includes a subset of information of a table that includes accounts for multiple different clients. By using the custom index, the query engine can filter the results more efficiently that if solely using the multiple client table.

Abstract: Methods and systems are provided for querying a database. One exemplary method for obtaining data from an on-demand database supporting one or more tenants involves obtaining model database statistics based on expected utilization information for a tenant and providing the model database statistics to the on-demand database. The on-demand database utilizes the model database statistics to generate a query plan and executes the query plan to obtain data from the model database statistics.

Abstract: Statistics mechanisms in multitenant database environments. A master statistics file is maintained in a multitenant database system. The master statistics file has statistics corresponding to multiple tenants within the multitenant database system. Statistics for a selected table within the multitenant database system are generated. The selected table corresponding to a selected tenant of the multitenant database system. The master statistics file is updated based on the generated statistics for the selected table.

Abstract: Methods and systems are provided for querying a database. One exemplary method for obtaining data from an on-demand database supporting one or more tenants involves obtaining model database statistics based on expected utilization information for a tenant and providing the model database statistics to the on-demand database. The on-demand database utilizes the model database statistics to generate a query plan and executes the query plan to obtain data from the model database statistics.

Abstract: Methods and systems are described for creating a custom index in a multi-tenant database environment. In one embodiment, a method includes obtaining query for a multi-tenant database that is recommended as a candidate for creating an additional filter, evaluating the query against criteria to determine whether to select the query for creating the additional filter, and creating the additional filter for the query, if the query is selected.

Abstract: Methods and systems are provided for querying a database. One exemplary method for obtaining data from an on-demand database supporting one or more tenants involves obtaining model database statistics based on expected utilization information for a tenant and providing the model database statistics to the on-demand database. The on-demand database utilizes the model database statistics to generate a query plan and executes the query plan to obtain data from the model database statistics.

Abstract: Methods and systems are provided for querying a multi-tenant database. One exemplary method of generating an improved query plan to a database from an input query submitted to an on demand database service over a network involves determining guidance information appropriate to the input query based at least in part on an identity of a source of the input query, determining a database hint for processing the input query based at least in part on the guidance information, and providing the database hint to the database to form the improved query plan, thereby enabling the database to return an improved result responsive to the input query based at least in part upon the database hint.