Machine learning version management method for a prediction service includes receiving a prediction request, determining application metadata for the request that defines routing logic and a machine learning framework version, determining model metadata for the request that defines at least one model and at least one model version, forwarding the prediction request to the at least one model with the at least one model version, and returning a prediction from the at least one model to a requestor.

A method conducted using a mobile device is disclosed. The method includes maintaining a plurality of software modules, wherein each software module of the plurality of software modules is built and executed independently of other software modules. The mobile device receives first interaction initiation data corresponding to a first transaction. In response to receiving the first interaction initiation data, a first subset of the plurality of software modules is selected via an application interface to execute the first transaction. The mobile device receives second interaction initiation data corresponding to a second transaction. In response to receiving the second interaction initiation data, a second subset of the plurality of software modules to execute the second transaction is selected via the application interface. The first subset of the plurality of software modules is different than the second subset of the plurality of software modules.

Disclosed herein is a system for improving the user experience in the face of a regression by returning resources that offer a service to a “last known good” upgrade. In other words, the state of the resources is reconfigured to scale back from recent upgrade(s), the deployments of which likely caused the regression, to a previous upgrade that is known to have little or no effect on the user experience. To identify a problem, the system collects performance data from different resource units that make up a cloud-based platform. The performance data is collected for each upgrade event in a sequence of upgrade events that are currently deployed or being deployed. The system continually tracks and analyzes qualification data collected for each of the deployed upgrade events. The system can tag an upgrade event as the last known good upgrade event when the collected qualification data satisfies predefined qualifications.

A method for managing an application snippet includes: obtaining a first application snippet (S210); determining first classification information of the first application snippet (S220); determining that classification information of a first composite application on the terminal device matches the first classification information (S230); and adding the first application snippet to the first composite application (S240). According to the method for managing an application snippet, a large quantity of application snippets can be effectively managed.

Versions of a service not reachable by a set of service requestors that use the service are removed. Multiple, different versions of a service are stored, along with metadata associated with the multiple, different versions of the service. The metadata is examined to determine one or more of the multiple, different versions of the service that are not reachable by the set of service requestors that use the service. Those versions are deleted.

An improved board management controller provides a stable operating environment. The board management controller comprises a flash memory device and a microprocessor. The flash memory device is partitioned into a boot area, a first partition and a second partition in advance. The boot area stores a bootup firmware, the first partition is formatted as a first file system, the second partition is formatted as a second file system, and the first operating system image file and the first software application are stored in the first file system. The second operating system image file and the second software application are stored in the second file system. Multiple advantages are provided by the board management controller, comprising flexible space configuration, the ability to recover from damaged faults, and high data reliability. The efficiency of the operation has also been improved to speed up the startup of the board management controller.

Systems and methods for version control of pipelined enterprise software are disclosed. Exemplary implementations may: store information for executable code of software applications that are installed and executable by users, receive first user input from a first user that represents selection by the first user of a first software pipeline for execution; receive second user input from a second user that represents a second selection by the second user of a second software pipeline for execution, wherein the second software pipeline includes different versions of software applications that are included in the first software pipeline; facilitate execution of the first software pipeline for the first user; and facilitate execution of the second software pipeline for the second user at the same time as the execution of the first software pipeline for the first user.

Generally, the present disclosure is directed to systems and methods that include and/or leverage a responsible artificial intelligence controller to provide context-specific responsible artificial intelligence control. The responsible AI controller (RAI Controller) is a solution to the problem of a single model that cannot be simultaneously responsible to all its users. The controller embraces the notion of demographic and societal diversity, and it gives the user control over their interaction with the AI system.

This application provides a mobile service upgrade method. In the method, each service kit in a mobile service is independently packaged into an APK based on a plugin-based idea, and then the kit is upgraded in real time when a user uses an application and the application invokes the kit. Compared with a full silent upgrade of the mobile service, this method shortens upgrade time and improves upgrade flexibility. Further, the APK of the kit includes a dependency relationship between the kit and a dependent kit, and the kit and the dependent kit may be upgraded together during upgrading. This avoids an invocation failure caused because a version cannot meet a requirement.

The present disclosure relates to a messaging method for a hardware acceleration system. The method includes determining exchange message types to be exchanged with a hardware accelerator in accordance with an application performed by the hardware acceleration system. The exchange message types indicate a number of variables, and a type of the variables, of the messages. The method also includes selecting schemas from a schema database. The message type schemas indicates a precision representation of variables of messages associated with the schema. The selected schemas correspond to the determined exchange message types. Further, the method includes configuring a serial interface of the hardware accelerator in accordance with the selected schemas, to enable a message exchange including the messages.