MLOps for Edge RAG Systems

Overview

View Diagram: MLOps Pipeline for Edge RAG

Figure 1: Complete MLOps pipeline for Edge RAG model lifecycle management

Implement machine learning operations for managing model lifecycle in production edge RAG deployments.

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MLOps Pipeline

graph LR
    subgraph Dev[Development]
        Data[Data Collection] --> Train[Model Training]
        Train --> Validate[Validation]
        Validate --> Test[Testing]
    end

    subgraph Deploy[Deployment]
        Test --> Package[Edge Packaging]
        Package --> Version[Version Control]
        Version --> Rollout[Staged Rollout]
    end

    subgraph Monitor[Monitoring]
        Rollout --> Perf[Performance Tracking]
        Perf --> Drift[Data Drift Detection]
        Drift --> Quality[Quality Metrics]
    end

    subgraph Retrain[Retraining]
        Quality --> Trigger{Retrain?}
        Trigger -->|Yes| Auto[Auto Retrain]
        Trigger -->|No| Monitor
        Auto --> Gates[Validation Gates]
        Gates --> Approve[Approval]
        Approve --> Data
    end

    style Dev fill:#E8F4FD,stroke:#0078D4,stroke-width:2px,color:#000
    style Deploy fill:#FFF4E6,stroke:#FF8C00,stroke-width:2px,color:#000
    style Monitor fill:#F3E8FF,stroke:#7B3FF2,stroke-width:2px,color:#000
    style Retrain fill:#D4E9D7,stroke:#107C10,stroke-width:2px,color:#000

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Model Management

Training

• Data collection
• Model training
• Validation
• Testing

Deployment

• Edge packaging
• Version control
• Staged rollout
• Monitoring

Monitoring

• Performance tracking
• Data drift detection
• Model quality metrics
• User analytics

Retraining

• Trigger conditions
• Automatic retraining
• Validation gates
• Deployment approval

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Operational Excellence

• Automated pipelines
• Governance controls
• Audit trails
• Disaster recovery

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See also: Architecture

Lab