US 12,131,242 B1
Fleet and asset management for edge computing of machine learning and artificial intelligence workloads deployed from cloud to edge
Pradeep Nair, Greebrae, CA (US); Pragyana K Mishra, Seattle, WA (US); Anish Swaminathan, Greebrae, CA (US); and Janardhan Prabhakara, Greebrae, CA (US)
Assigned to Armada Systems Inc., Greebrae, CA (US)
Filed by Armada Systems Inc., Greebrae, CA (US)
Filed on Sep. 5, 2023, as Appl. No. 18/461,473.
Int. Cl. G06N 20/20 (2019.01); H04L 41/16 (2022.01)
CPC G06N 20/20 (2019.01) [H04L 41/16 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a user input indicative of a request to deploy a simultaneous localization and mapping (SLAM) machine learning workload to a containerized edge data center unit at an edge location, wherein the containerized edge data center unit implements the SLAM machine learning workload locally at the edge location to provide routing and control instructions to one or more autonomous robotic devices connected to a local edge network implemented by the containerized edge data center unit;
obtaining a pre-trained SLAM machine learning model corresponding to the request, wherein the pre-trained SLAM machine learning model is configured to determine routing and control instructions for the one or more autonomous robotic devices based on image data and sensor data obtained from corresponding edge sensors associated with the containerized edge data center unit and connected to the local edge network;
adjusting, a subset of parameters of the pre-trained SLAM machine learning model to perform model optimization for the containerized edge data center unit, wherein the model optimization is based on the request and information indicative of the corresponding edge sensors associated with the containerized edge data center unit;
transmitting the pre-trained SLAM machine learning model to the containerized edge data center unit;
receiving, from the containerized edge data center unit, one or more batch uploads of inference results information associated with the routing and control instructions generated by the containerized edge data center unit with the pre-trained SLAM machine learning model for controlling the one or more autonomous robotic devices;
generating an updated SLAM machine learning model by retraining or finetuning the pre-trained SLAM machine learning model based on the inference results information and inference features further included in the one or more batch uploads; and
transmitting the updated SLAM machine learning model to the containerized edge data center unit, wherein transmission of the updated SLAM machine learning model is responsive to receiving the one or more batch uploads of inference results information.