Aspects of this disclosure relate to using artificial intelligence (“AI”) to control integration of software developed by a third-party into an enterprise computing environment subject to more rigorous regulatory and security testing than typically provided by the third-party. AI software development automation tools will deploy third-party scripts to edge servers. Deploying to edge servers allows for integration of the third-party tags into testing environment pipelines. Local storage associated with third-party tags will be at a top-level domain, allowing third-party software tags to be treated as first party without the reputational and technical risks of cross-site storage.

In various example embodiments, a system and method are provided for a service data platform. The service data platform includes an application management API configured to receive software uploaded by a third party using a management application API. The service data platform also includes a plurality of graph servers configured to identify a group of lighting nodes to distribute the uploaded software and determine the uploaded software is safe for deployment to the identified group of lighting nodes. The service data platform further includes a device manager configured to distribute, using an administrative API, the uploaded software to the identified group of lighting nodes.

Systems and methods to utilize a machine learning model registry are described. The system deploys a first version of a machine learning model and a first version of an access module to server machines. Each of the server machines utilizes the model and the access module to provide a prediction service. The system retrains the machine learning model to generate a second version. The system performs an acceptance test of the second version of the machine learning model to identify it as deployable. The system promotes the second version of the machine learning model by identifying the first version of the access module as being interoperable with the second version of the machine learning model and by automatically deploying the first version of the access module and the second. version of the machine learning model to the plurality of server machines to provide the prediction service.

Disclosed are a method and server for providing an on-screen automatic response service using automatic pop-up through a voice call auto-reply. A method of providing an on-screen automatic response service (ARS) through a voice call auto-reply includes receiving a voice call from a mobile terminal, checking whether a software development kit (SDK), which is necessary to execute program location information (URL) for providing the ARS on the mobile terminal directly without user intervention, is installed, and executing the program location information (URL) directly without user intervention to provide the ARS or providing the program location information (URL) to the mobile terminal and providing the ARS according to an execution command for the program location information (URL) received from a user depending on whether the software development kit is installed. Accordingly, a separate database for checking the installation of a separate SDK is not required, and multiple steps for inquiry are not required. Thus, it is possible to simplify configuration, improve service speed, and also reduce operating costs.

A computer system is described. This computer system may implement a controller for multiple different types of computer network devices (CNDs), such as: an access point, a switch, a router, and a dataplane. Moreover, the computer system may have a common framework for program modules (with sets of program instructions) associated with the different types of CNDs. Furthermore, configuration and management of a given type of CND using the program modules may be specified by metadata associated with the given type of CND. Additionally, the common framework may include a unified protocol layer for the program modules, and one or more of the program modules may be modified or configured via the unified protocol layer using a common communication Alternatively or additionally, the computer system may communicate with the different types of CNDs via the unified protocol layer using a second common communication protocol.

A downhole network includes a tool string such as a drill string or marine riser. The tool string may comprise components in communication with a surface control unit, SCU, storing executable code associated with the downhole tool string components. A downhole network may be in communication with the SCU and the downhole tool string components. The SCU may be in communication with a surface multi-network controller which is in communication with the downhole network and in communication with a remote downhole tool string, a remote downhole network, and a remote tool string component. The remote downhole tool string or marine riser may comprise a plurality of interconnected wired tubulars. The wired tubulars may comprise inductive couplers comprising reinforced MCEI troughs. The surface multi-network controller may be in communication with a mobile device comprising a restricted subscriber identification module (SIM). The SIM may restrict communications to the respective downhole networks.

Systems, methods, and devices, including computer-readable media, for managing operation of devices in complex systems and changing environments. In some implementations, a server system stores data indicating management plans for each of a plurality of different devices, each management plan indicating a device-specific set of program states for programs in a predetermined set of programs. The server system alters the management plans and enforces interdependence of the programs, and the server system generates a customized instruction that alters operation of the device according to the device-specific set of program states assigned in the altered management plan for the device. The server system causes each device to perform one or more operations of the device determined according to the device-specific set of program states assigned in the altered management plan for the device.

A system is described. The system includes a distributed ledger peer-to-peer blockchain fabric comprising a plurality of peer nodes, including a first peer node to receive a workload package, examine the workload package to determine a role of the first peer node within a cluster configuration of a first set of the plurality of peer nodes and execute the workload package at resources included in the first peer node.

Systems and methods to utilize a machine learning model registry are described. The system deploys a first version of a machine learning model and a first version of an access module to server machines. Each of the server machines utilizes the model and the access module to provide a prediction service. The system retrains the machine learning model to generate a second version. The system performs an acceptance test of the second version of the machine learning model to identify it as deployable. The system promotes the second version of the machine learning model by identifying the first version of the access module as being interoperable with the second version of the machine learning model and by automatically deploying the first version of the access module and the second version of the machine learning model to the plurality of server machines to provide the prediction service.

A processor and a memory connected to the processor store instructions executed by the processor to collect playback statistics including audio attributes or video attributes associated with playback of specified content from multiple devices, aggregate the playback statistics, and establish a playback configuration for the specified content based on the playback statistics. The specified device here may be a type of content player or server.