Deploying and managing a large network of AI infrastructure at the edge remains a challenge for organizations especially when the edge devices are spread across geographically dispersed locations. From remote debugging, software management and maintenance to system monitoring, a centralized management suite like FleetTrackr provides a simplified and secured method to provision, manage, maintain, monitor, and update thousands of appliances, entirely over-the-air. For example, Ultron could be widely distributed across vast distances when deployed for industrial automation and FleetTrackr can be used to streamline management.
There are six key components in FleetTrackr provisioned by the FleetTrackr Management Suite:
Order Management: Keep track of order status and manage device inventory
Device Management: Easily monitor device metrics and device status, get access to crucial device performance KPIs based on historical data. Access device documents, specification sheets, and user manuals for ease of deployment.
Container Management: Update or restart your container with a single click and create a group of devices to provision containers easily.
Firmware-Over-The-Air: Firmware update, backup and recovery through remote access
Issue Management: Group edge devices and raise tickets when a device goes faulty, create and manage site leaders and teams to resolve tickets, schedule regular device maintenance tasks, and get access to KPIs on ticket history.
Predictive Maintenance: Detect hardware/software anomalies through automated anomaly detection and classification, predict time-to-failure
In short, FleetTrackr is readily available to be used for deploying and managing Ultron at scale. Instead of spending weeks planning and executing deployment plans, administrators can upgrade AI solutions, manage applications, update system firmware and software, streamline operations and administrative tasks, and monitor the health metrics of the fleets from a single management panel.