The federal civilian agency guide
To adaptable and trusted enterprise MLOps
Download the white paper
How can federal civilian agencies scale AI and data science initiatives securely, reliably, and efficiently to unlock successful, measurable outcomes? Scaling AI across federal ecosystems requires overcoming hurdles such as fragmented or inaccessible data, legacy infrastructure, and complex governance requirements. And the consequences of ineffective AI adoption include operational inefficiencies, service delays, and eroded public trust.
For virtually all these obstacles, MLOps best practices are the answers to efficiently building AI solutions that are consistently scalable, reliable, and maintainable. Read this white paper to understand how a robust MLOps platform orchestrates and unifies MLOps best practices:
- Complex AI workflows and operational needs
- Governance and building public trust in AI
- Resource constraints and enabling the workforce
- Key characteristics of MLOps for federal civilian agencies
- Public-sector agency MLOps in practice: Four use cases
