Landing AI announced it has joined Nvidia Metropolis, a program designed to nurture and bring to market a new generation of applications and solutions that make the world’s most important spaces and operations safer and more efficient with advancements in AI vision.
Landing AI unleashes the next era of AI with its pioneering data-centric approach that brings the benefits of AI to manufacturers that lack big datasets associated with traditional AI. Its LandingLens software streamlines the implementation of industrial automated inspection systems using deep learning AI technology. Many LandingLens customers deploy at the edge using the Nvidia Jetson edge AI platform. Joining the Metropolis program ensures a seamless experience for customers accelerating AI performance and edge deployments to further improve quality control and more in manufacturing and industrial applications.
“Having successfully deployed the Nvidia Jetson platform at customer sites, we are thrilled to be part of Nvidia Metropolis. As LandingLens becomes the choice for manufacturers looking to optimise efficiency, we look forward to working closely with Nvidia and our system integration partners,” said Carl Lewis, Senior Director of Customer Success & Strategic Partnerships, Landing AI.
Nvidia Metropolis is an application framework that makes it easier and more cost-effective for enterprises, governments, and integration partners to leverage world-class AI-enabled solutions to improve critical operational efficiency and safety problems. The Nvidia Metropolis ecosystem contains a large and growing breadth of partners who are investing in the most advanced AI techniques and most efficient deployment platforms and using an enterprise-class approach to their solutions.
Partners have the opportunity to gain early access to Nvidia platform updates to further enhance and accelerate their AI application development efforts. Further, the program offers opportunities for partners to collaborate with industry-leading experts and other AI-driven organisations.
