New Features For Edge AI On NVIDIA Fleet Command
NVIDIA's Fleet Command AI management platform is seeing some new features for the focused support on remote location edge AI control and monitoring.
Created on July 18|Last edited on July 18
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Fleet Command, NVIDIA's toolbox software for the broad deployment and management of AI applications at scale, predominantly leveraging cloud computing, is seeing a platform update to add new tools for on-the-edge AI application management. These new tools for edge AI should be useful to any IT teams who need to manage locally-running AI applications positioned on-site across multiple locations.
What's new in NVIDIA Fleet Command?
The new edge AI tools in NVIDIA Fleet Command includes:
- a new remote management tool for easy edge AI system monitoring.
- support for multi-instance GPU provisioning so that locally-running GPUs may be partitioned for maximum efficiency, and
- partnerships with three external companies to enhance edge AI management.
The new advanced remote management system will let IT teams easily manage AI systems in remote locations. With sophisticated security systems protecting any connections through the management software, any attempted hijacking of sensitive information or unauthorized access is mitigated.
The remote management system also adapts well to any edge setup or configuration.
Multi-instance GPU provisioning lets one NVIDIA GPU be partitioned into independent instances so that multiple AI models can have access to the resources they need to run efficiently.
Three new company partners are collaborating with NVIDIA and the Fleet Command software in this edge AI update.
Domino Data Lab brings a new integrated MLOps platform for data scientists to collaborate when managing AI systems.
Milestone Systems who developed Bridge AI, a system for handling multi-camera video feeds, has integrated it into Fleet Command.
IronYun also brings their own video management system which can connect with thousands of IP cameras.
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