MLOps for Defense/ MLOps-D

Definition

MLOps for Defense is lifecycle practices for building, testing, approving, deploying, monitoring, and updating military AI. In defense applications, it turns models from experiments into maintainable mission capabilities. The hard part is slow approval cycles, version drift, and poor feedback from operations, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as the factory layer behind KhanBMS modular AI updates, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

AI operations discipline

Operational value

Turns models from experiments into maintainable mission capabilities

Primary risk

Slow approval cycles, version drift, and poor feedback from operations

KhanBMS role

The factory layer behind KhanBMS modular AI updates

Related terms

• Edge Model Registry
  Versioned catalog that tracks which models, adapters, signatures, and policies are deployed to tactical nodes.
• Model Observability
  Monitoring of model inputs, outputs, drift, latency, confidence, and failures after deployment.
• AI Bill of Materials (AIBOM)
  Inventory of models, datasets, adapters, tools, dependencies, licenses, and provenance in an AI system.
• DevSecOps
  Practice of integrating security into continuous software delivery pipelines.