A system and a method are disclosed to configure networking components, including in response to a first configuration command, creating a running configuration for a host computer, saving the running configuration in a configuration file stored in a temporary directory, and in response to receiving a second configuration command to make the running configuration persistent, copying the configuration file to a persistent directory.

In an approach to API versioning, annotations are provided to differentiate among two or more versions of an API. Statistics associated with the two or more versions of the API are collected. A call from a client to access one of the two or more versions of the API is intercepted. A best-fit version for the client is identified based on employing analytical elements on the collected statistics. API execution is re-routed to the identified best-fit version during run-time.

Among other things, techniques for securely booting processors in a vehicle are described. An apparatus comprises a circuit coupled to one or more processors of a vehicle and managing a secure boot process for the processors. The circuit receives an indication that the vehicle has been powered on and sends, to a network server, a request for boot files for the processors of the vehicle. In response, the circuit receives, from the server, most recent versions of boot files respectively corresponding to the processors, wherein each boot file includes a digital signature of a trusted authority. In response to obtaining the most recent versions of the boot files, the circuit sequentially boots the processors using the respective boot files, wherein each processor executes a corresponding boot file upon verifying authenticity of the digital signature in the boot file using a corresponding class authentication key.

A method and system for deploying an application from a deployment source to a device may include determining if the application is available for deployment to the device from a plurality of deployment sources, upon determining that the application is available for deployment from the plurality of deployment sources, identifying one of the plurality of deployment sources as a preferred deployment source for deploying the application; and deploying the application from the preferred deployment source to the device. Deploying the application may include installing the application, loading one or more user interface elements associated with the application and activating the application.

Systems and methods are provided for servicing inference request by one of multiple machine learning models attached to a deployment cluster. The API server of a deployment cluster is not tightly coupled to any of multiple machine learning models attached to the deployment cluster. Upon receiving an inference request, the deployment cluster can retrieve the configuration parameters, including serialization formatting, for a target model identified in the inference request. The deployment cluster can utilize the retrieved parameters to service the inference request and return the results to a business system application.

This disclosure relates to an improved software development platform that enables the dynamic generation and deployment of advanced applications such as electronic forms and/or other applications. The platform includes graphical user interfaces that enable users to drag-and-drop or otherwise insert components into a development window to define the content, functionality, workflow, background processes, and other criteria for the electronic forms. The components may correspond to pre-defined or pre-packaged software bundles that can be integrated into the electronic forms to define input fields, design layouts, application functions, preconfigured templates, and background processes. After electronic forms are completed, options can be selected to deploy the forms in a cloud-based environment or in other ways. This disclosure is also related to improved software development and deployment platforms that provide version control components that are configured to perform various functions associated with managing, controlling, accessing, and/or comparing multiple versions of electronic forms and associated form components.

An information handling system may include at least one processor; and a non-transitory memory coupled to the at least one processor. The information handling system may be a node of an information handling system cluster may be configured to be imaged while the node is operational within the information handling system cluster by: a node installation service executing on the node creating a partition on a storage medium of the node; the node installation service receiving an image bundle from a remote information handling system via a network and storing the image bundle on the partition; and the node installation service causing the node to reboot, wherein the rebooting is configured to install a new version of a management system to the node from the image bundle.

Approaches presented herein enable hot upgrading a microservices sequence in a cloud computing environment. More specifically, a next microservice of microservice subsequence in a running sequence is obtained, in response to a message to invoke the microservice or subsequence. The running microservice sequence includes at least one unexecuted microservice or subsequence that is to be hot upgraded. The running microservice sequence is generated based on a sequence that is to be hot upgraded which comprises an ordered list of microservices and/or subsequences. The approach may include determining the status of a next microservice or subsequence. The approach may further include invoking the next microservice or subsequence in the running sequence, in response to the status of the next microservice or subsequence being upgrade-complete.

Methods and systems for controlling an autonomous machine. The autonomous machine has sensors generating input data, while the controller includes two or more neural networks that inference using the input data and generate output data. The neural networks can be trained using an identical set of training data set. The output data from each of the neural networks are monitored to ensure that the integrity of the operation is maintained by, for example, the output data from one neural network is compared with the output data from another neural network to verify the consistency. If the comparison yields that the integrity of the system is not maintained at an acceptable level, the controller can stop using the output in controlling the autonomous machine.

An information processing system including a device and a client terminal which is able to create action configuration data for implementing a plurality of functions in the device includes a first management unit configured to manage a first version of firmware for implementing a plurality of functions in the device and a second version of a schema usable in the firmware of the first version, a second management unit configured to manage the action configuration data, which has been created based on information managed by the first management unit, and the second version, and a transmission unit configured to, in response to a request from the device, specify the action configuration data managed by the second management unit and to transmit the specified action configuration data and the first version to the device.