Domino Data Labs Blogs
Mission success: How Domino automates the CDAO AI test and evaluation framework
This blog is about how the DoD’s CDAO guidance on Test and Evaluation of AI Models can be put into practice for real mission environments. It breaks down the six core areas of trustworthy AI, performance, testing methods, data, models, context, and documentation, and explains how Domino’s Enterprise AI Platform supports each one. Using examples like the Navy’s Project AMMO and Lockheed Martin, it shows how agencies can evaluate, govern, and deploy AI models more securely, more efficiently, and even in air-gapped or edge environments.
Transform AI readiness into operational impact for government
MIT’s latest report highlights that while many AI initiatives struggle to move from experimentation into real-world use, government agencies have a major opportunity to focus on operationalizing AI effectively. Domino and NVIDIA address this gap by providing secure, governed, and scalable infrastructure that turns AI models into deployable systems — from development to monitoring and retraining. Together, they enable federal teams to build, manage, and deploy mission-ready, high-performance AI systems across secure and hybrid environments.
How the AI Action Plan helps solve federal data science’s biggest challenges
This blog is about how the White House AI Action Plan is pushing federal agencies to adopt AI faster by improving collaboration, streamlining approvals, and reducing duplicate work. It explains how platforms like Domino support this by enabling secure cross-agency model sharing, controlling costs with open models, and automating governance to speed up deployment. The Navy’s Project AMMO is highlighted as an example of how these approaches can reduce AI deployment timelines from months to days.
Operational AI: Scaling AI in government agencies
This blog is about the challenge government agencies face in moving AI from small pilots to real, mission-ready deployments. It explains why scaling requires standardized MLOps pipelines, strong governance, and architectures that work in low-connectivity, edge environments. The core idea is to make AI repeatable and reliable across missions so agencies can deploy once and run anywhere.
Future-ready federal AI: Meeting new OMB guidelines with Domino
This blog is about how Domino helps government agencies meet the requirements in OMB M-25-21 and M-25-22 by providing centralized AI governance, transparency, and secure environments for both traditional and generative AI. It explains how Domino supports oversight, risk management, inventory reporting, and reusable pipelines, while also avoiding vendor lock-in and enabling collaboration across mission, procurement, and technical teams. The core message is that agencies can move faster without sacrificing compliance, security, or control.