U.S. Navy makes mine detection intelligence more trustworthy with Domino

The U.S. Navy used ML models for underwater target threat detection via unmanned underwater vehicles, but couldn't easily monitor or improve model performance post-deployment as underwater conditions and enemy tactics changed. Through Project AMMO (Accelerated Machine Learning for Maritime Operations), the Navy partnered with the Defense Innovation Unit and integrated Domino's Enterprise AI Platform into an MLOps pipeline for mine countermeasures in AWS GovCloud (DoD IL5). Deployment time for automatic target recognition models fell from 6 months to 6 days, and retraining time fell from 12 months to 6 days. Commodore Shaun Lieb said the team 'demonstrated the ability to deploy and update our automatic target recognition models at the speed of operational relevance.'

Domino Data Lab is an enterprise MLOps platform built for regulated industries — the sanctioned sandbox where biopharma teams run reproducible experiments under GxP/21 CFR Part 11 guardrails.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.