Wallaroo Selected By US Space Force To Solve Edge Model Deployment Challenges In Space

Wallaroo Labs announced that they have been selected by SPACEWERX, the innovation arm of the US Space Force (USSF), to solve edge model deployment challenges specific to On-Orbit Servicing, Assembly, and Manufacturing (OSAM) missions. This fully funded Phase 1 project in collaboration with Catalyst Campus (CCTI) will look at edge model deployment challenges for use cases like satellite life extension, on-orbit refueling, active debris removal, and the reuse & recycling of materials to build the foundation for assembly and manufacturing in space.

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“This remarkable opportunity with the US Space Force speaks to Wallaroo’s expertise to operationalize machine learning in even the most demanding environments requiring the highest degree of security and performance”

“The mission of the United States Space Force (USSF) is to organize, train, and equip Guardians to conduct global space operations that enhance the way our joint and coalition forces fight, while also offering decision makers military options to achieve national objectives,” said Dr. Joel Mozer, Director Science, Technology & Research at SPACEWERX. “To do this effectively, we must invest in AI and ML capabilities that can be deployed in the cloud and at the edge. Wallaroo has demonstrated their AI/ML Enterprise Platform, and I believe this platform – with its uniquely modern, interoperable, and integrated architecture – is positioned exceptionally well to deliver game-changing capabilities to the USSF.”

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“This remarkable opportunity with the US Space Force speaks to Wallaroo’s expertise to operationalize machine learning in even the most demanding environments requiring the highest degree of security and performance,” said Vid Jain, CEO & Founder of Wallaroo.

In addition to their work with the public sector, including with the US Air Force, Wallaroo is also working with several Fortune 500 companies to help them deploy and manage their machine learning models at scale, generating better performance and observability over their AI/ML initiatives.

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