MLOps for Distributed Inference
Production inference requires disciplined model lifecycle management. This course covers MLOps adapted for DOSFI's distributed inference fabric: model registration and versioning, continuous training pipelines, model deployment patterns, A/B testing and canary inference, drift detection and monitoring, and automated retraining triggers. Equivalent to AWS ML Engineer Associate and Microsoft AI-102, purpose-built for mesh-native inference.
What you'll learn
- Model lifecycle management on the distributed mesh
- Model registration, versioning, and registry design
- Continuous training pipelines with fresh mesh data
- Deployment patterns: shadow, canary, and multi-model routing
- A/B testing for inference endpoints
- Drift detection: data drift, concept drift, and performance degradation
- Automated retraining triggers and model rollback
All DOSFI educational content is produced under the Sovereign Closed-Core / Open-Surface model. DOSFI University is theory only — conceptual architecture, mathematics, and design theory. DOSFI Academy is practical only — SDK usage, ABI integration, and hands-on labs. Both tracks draw exclusively from open-surface materials: SDKs, ABIs, documentation, tutorials, examples, and conceptual explanations. Internal implementation details of the Runtime, Scheduler, Router, Readiness Engine, Privacy, Power, Thermal, Vital, DIU, and MNE subsystems are never revealed by either track. The DOSFI Core engine remains permanently sealed, preserving DOSFI's sovereignty, mission-lock, and non-replicable architecture.
Curriculum
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