Armada Systems Inc.

Systems and techniques are provided for processing streaming data. For example, a process can include obtaining, by an edge device, a plurality of data streams corresponding to objects, activities, or events registered in an edge environment associated with the edge device. One or more machine learning networks implemented on the edge device can generate a plurality of features and/or embeddings corresponding to each respective data stream of the plurality of data streams. The edge device can determine a subset of salient content from the plurality of data streams, based on analyzing the plurality of features and/or embeddings generated for each respective data stream. The edge device can generate index information corresponding to the determined subset of salient content. The subset of salient content and the generated index information can be stored locally at the edge device for search and retrieval based on local queries received at the edge device.

Disclosed are systems and methods to control a robotic device using natural language commands. A natural language command may be received by a system. The system may convert the command into low-level machine controls and logic for implementation by a robotic device to achieve a desired action. In some instances, an API module may include mapping data to associate high-level commands with low-level machine controls. A language model may process the natural language command (input), high-level commands, and/or other information, such as system state, sensor observation data, parameters, etc., to determine one or more commands to execute by a robotic device and possibly logic for execution by the robotic device. The robotic device may receive the low-level machine controls and logic to cause the robotic device to perform the requested actions.

Disclosed are systems and methods to track an object in a scene even when the object is partially or wholly obscured by an artifact, such as another object. As the tracked object moves through the scene, frames of video are processed and used to refine and tune a diffusion model that predicts an appearance of the tracked object in future frames of the video as well as the appearance of artifacts in the frames of the video. Frames of the video may then be enhanced to illustrate the tracked object as if the tracked object were visible through the artifact.

A method can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration associated with at least one edge compute unit of the fleet. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

A method can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration associated with at least one edge compute unit of the fleet. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

A plurality of computational units provided within a housing of a grid-independent mobile data center apparatus are configured to implement an edge data center. An onboard energy generation system is deployable from the housing and generates electrical energy for powering at least the plurality of computational units. A cooling system is powered by the onboard energy generation system. One or more battery systems store electrical energy generated by the onboard energy generation system. A communications system includes one or more satellite transceivers each associated with a satellite internet constellation and configured to provide data connectivity between the satellite internet constellation and the plurality of computational units. One or more propulsion systems are coupled to the housing and configured to move the grid-independent mobile data center apparatus within a surrounding environment utilizing electrical energy from the onboard energy generation system.

A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.

Systems and techniques are provided for multi-WAN routing and management at the edge. A multi-WAN network device can configure first and second backhaul connections between a local edge network and a remote WAN, the first and second backhaul connections implemented using first and second communication modalities different from one another and included in a plurality of communication modalities of the multi-WAN network device. A first containerized application on the multi-WAN network device can obtain telemetry or monitoring information generated by a satellite internet constellation terminal in the local edge network, and can transmit the information to a cloud management entity using one or more of the first or second backhaul connections of the multi-WAN network device. The multi-WAN network device can implement a network configuration adjustment for one or more of the first or second backhaul connection based at least in part on the telemetry information or monitoring information.

Systems and techniques are provided for self-healing at the edge. A plurality of faults can be detected for an edge device based on monitoring log information of the edge device, with remediation information indicative of remediation actions performed for individual faults or combinations of faults included in the plurality of faults. A hierarchical fault tree data structure can be generated mapping between the plurality of faults and the remediation information, each fault comprising root or child node of the fault tree, and each remediation action comprising a leaf node of the fault tree. An indication of one or more faults can be provided to a self-healing machine-learning or artificial intelligence engine configured to generate a corresponding fault remediation action based on traversing the hierarchical fault tree according to the indicated one or more faults. The fault remediation action can be output for self-healing of the indicated one or more faults.

Systems and techniques are provided for resiliency and redundancy for provisioning and/or configuring an edge compute unit. Configuration information can be obtained for provisioning an edge device with a plurality of nodes each associated with a respective rack of a plurality of racks. A first subset of the plurality of nodes can be provisioned, based on the configuration information, as a management cluster for workloads deployed to the edge device, the management cluster provisioned to include multiple redundant management control plane nodes distributed across different racks of the plurality of racks. A workload cluster can be provisioned on a remaining portion of the plurality of nodes, the workload cluster provisioned to include: multiple redundant workload control plane nodes distributed across different racks of the plurality of racks, and a respective plurality of worker nodes provisioned on each rack of the plurality of racks.