Jane Edwards
The Defense Innovation Unit and the U.S. Navy are awarding success memos to five vendors that have developed prototypes and fielded machine learning operations tools designed to modify, track and redeploy ML algorithms on unmanned underwater vehicles used to detect underwater target threats.
DIU said Monday that Arize AI, Domino Data Lab, Fiddler AI, Latent AI and Weights & Biases will move into production of their MLOps tools developed under Project Automatic Target Recognition using Machine Learning Operations for Maritime Operations, also known as Project AMMO.
In the fall of 2022, the five companies secured agreements from DIU to develop prototypes of their proposed MLOps tools.
According to DIU, the capabilities provided by the vendors have enabled the Navy’s machine learning developers, such as Northrop Grumman, SeeByte and Johns Hopkins Applied Physics Laboratory, to focus on creating and refining automatic target recognition models.
“The partnership between the U.S. Navy and DIU has opened the pathways to commercial innovation and has produced a MLOps toolset that ensures our solutions adapt and evolve at the speed of tactical relevance, placing an enduring capability in the hands of the warfighter,” said Commander Eli Ford, principal assistant program manager, Underwater Explosive Ordnance Disposal, PMS 408 (Expeditionary Missions).
“Nowhere was this more apparent than at an Integrated Battle Problem in the Fall of 2023 where sensor data was collected, new models were trained against an expanded threat environment, and updated detection models were deployed through the AMMO ecosystem back into the hands of the operator,” Ford added.
During the fall 2023 exercise, the pipeline of AMMO MLOps tools decreased by 97 percent the time needed to update the machine learning models deployed on UUVs.
According to DIU, the toolset is expected to support other naval missions and use cases.
