Abstract: An information handling system receives a request for an artificial intelligence service that is associated with an artificial intelligence model and retrieves a manifest associated with the artificial intelligence model. The system also determines a current system capacity of the information handling system and determines a workload segmentation according to the manifest based on the current system capacity of the information handling system.

Abstract: An information handling system stores a multiple machine learning (ML) models. The system receives a job request that includes input parameters and constraints for a job associated with the job request. Based on the input parameters and the constraints, the system identifies first and second ML models available to execute the job. The system also generates a first score for the first ML model and a second score for the second ML model. Based on the first score being greater than the second score, the system provides the job to a compute resource associated with the first ML model.

Abstract: An information handling system receives a request for an artificial intelligence interaction and determines requirements of an artificial intelligence model associated with the request for the artificial intelligence interaction. The information handling system also evaluates a capability of the information handling system based on processing units of the information handling system and discovers artificial intelligence-capable information handling systems in a local network and a private extended network. In addition, the information handling system generates a list of the discovered artificial intelligence-capable information handling systems in the local network and the private extended network. The list of the discovered artificial intelligence-capable information handling systems includes the information handling system.

Abstract: An information handling stores machine learning (ML) models and quality of service (QoS) data. The system performs a registration operation for multiple model management frame (MMF) core contracts and multiple supporting MMF converted modules (MCMs). Each different one of the MMF core contracts is supported by a corresponding different one of the MCMs. The system receives an application programming interface (API) request. In response to the API request, the system performs a learn operation for a first ML model and determines that a first MCM is to be used to respond to the API request. The system determines that the first ML model is associated with the first MCM and executes the first ML model via the first MCM to generate output data for a response to the API request.

Abstract: An information handling system stores an artificial intelligence (AI) model hub, and receives a request to execute an AI model within an application. In response to the request, the system collects performance related data from a plurality of AI models. Based on the collected performance related data, the system determines a workflow for the application. Based on the workflow, the system outputs a recommended AI model to be executed within the application.

Abstract: An information handling system launches an installer of a software package at an information handling system and the software package includes an artificial intelligence model plugin and a dependency. The information handling system also determines whether the artificial intelligence model plugin can be executed on the information handling system. In addition, the information handling system installs the artificial intelligence model plugin in response to a determination that the artificial intelligence model plugin can be executed on the information handling system. Further, the information handling system loads the artificial intelligence model plugin and the dependency in an isolated load environment of a model management framework at runtime.

Abstract: An information handling system converts an artificial intelligence model to be compatible with an endpoint management service and determine a dependency on the artificial intelligence model. The information handling system also generates a software package that includes a model management framework associated with the artificial intelligence model. The software package is compatible with the endpoint management service, and includes the converted artificial intelligence model and the dependency. In addition, the information handling system deploys the software package to a client device that is managed by the endpoint management service.

Abstract: An information handling system stores multiple usage samples and multiple datasets for a machine learning (ML) model. The system trains the ML model and provides the ML model to a model management framework (MMF). An MMF core of a processor within the MMF executes the ML model. The processor determines whether the execution of the ML model has a low confidence level. In response to the execution of the ML model having the low confidence level, the processor updates the usage samples for the ML model. In response to the execution of the ML model having a high confidence level, the processor stores the ML model in the memory.

Abstract: This disclosure provides systems, methods, and devices for network management. In a first aspect, a method of managing communications links includes determining a latency sensitivity for first data for transmission between a first device and a second device and determining whether the latency sensitivity satisfies a criteria. When the criteria is met, the method includes determining a plurality of jitter likelihood values corresponding to a plurality of links between the first device and the second device; and configuring multi-link operation between the first device and the second device using a first link of the plurality of links and a second link of the plurality of links, wherein the first link and the second link are determined based on the plurality of jitter likelihood values. Other aspects and features are also claimed and described.

Abstract: A plant growing system for growing plants in a controlled environment includes at least one cabinet assembly. Each of the at least one cabinet assembly a housing and a drawer which is removably positioned in the housing. At least one plant pot for holding a plant is positionable on a support tray which is positionable in the drawer. A light emitter is mounted in the housing and emits light toward the plant when activated.

Abstract: A plant growing system for growing plants in a controlled environment includes at least one cabinet assembly. Each of the at least one cabinet assembly a housing and a drawer which is removably positioned in the housing. At least one plant pot for holding a plant is positionable on a support tray which is positionable in the drawer. A light emitter is mounted in the housing and emits light toward the plant when activated.

Abstract: This disclosure provides systems, methods, and devices for network management. In a first aspect, a method of managing communications links includes determining a latency sensitivity for first data for transmission between a first device and a second device and determining whether the latency sensitivity satisfies a criteria. When the criteria is met, the method includes determining a plurality of jitter likelihood values corresponding to a plurality of links between the first device and the second device; and configuring multi-link operation between the first device and the second device using a first link of the plurality of links and a second link of the plurality of links, wherein the first link and the second link are determined based on the plurality of jitter likelihood values. Other aspects and features are also claimed and described.

Abstract: This disclosure provides systems, methods, and devices for network management. In a first aspect, a method of transmitting delay-sensitive data with multi-link operation includes receiving, at a first device, first data for transmission to a second device; packaging, by the first device, the first data for transmission to the second device in a first set of packets; packaging, by the first device, the first data for transmission to the second device in a second set of packets; transmitting, by the first device, at least a portion of the first set of packets on a first shared resource; and transmitting, by the second device, at least a portion of the second set of packets on a second shared resource. Other aspects and features are also claimed and described.

Abstract: A physically rotatable display for a test and measurement apparatus is provided. The display of instrument information and data is automatically reconfigured based on the user selected orientation to provide optimal data display.

Abstract: A physically rotatable display for a test and measurement apparatus is provided.

Abstract: A remote display for a test and measurement apparatus is provided including a control panel. The control panel is removable and user-replaceable with an alternate control panel. The control panel can be connected to the remote display device to control and interact with the test and measurement apparatus, and to control the display of information on the remote display device.

Abstract: A test and measurement apparatus is provided including a control panel. The control panel is removable and user-replaceable with an alternate control panel. The control panel can be connected to the test and measurement apparatus or to a generic local computing device to control and interact with the test and measurement apparatus.