Module 4 - Production Edge RAG

Overview

Deploy and operate Retrieval-Augmented Generation (RAG) systems at production scale on edge infrastructure within sovereign cloud environments. Master real-world deployment patterns, optimization techniques, and enterprise operations.

Production Edge RAG Architecture

Production Edge RAG Architecture showing LLM inference, vector stores, and high availability patterns Figure 1: Enterprise-grade Edge RAG architecture with load balancing, GPU inference, and vector storage replication

Duration: 5-6 hours
Learning Tracks: Both Sales & Technical
Prerequisites: Level 200 Edge RAG completion

Learning Objectives

Sales Track

✅ Articulate production RAG use cases
✅ Understand performance and cost trade-offs
✅ Discuss enterprise SLA commitments
✅ Position consulting and professional services

Technical Track

✅ Design production RAG architectures
✅ Optimize inference and retrieval performance
✅ Implement MLOps for edge models
✅ Manage knowledge bases at scale
✅ Operate production RAG systems
✅ Implement disaster recovery and failover

Core Topics

Production Architecture → edge-rag-architecture-production.md
Performance Optimization → edge-rag-optimization.md
MLOps & Model Management → edge-rag-mlops.md

Production Architecture

Performance Optimization

MLOps Workflow

Advanced Topics

Scaling Patterns

Multi-node inference
Distributed retrieval
Load balancing strategies
Horizontal scaling considerations

Knowledge Base Management

Ingestion pipelines
Vector embedding updates
Semantic search optimization
Knowledge graph integration

Enterprise Operations

SLA management
Performance monitoring
Cost tracking
Capacity planning

Recommended Learning Path

Module Duration: 10-12 hours
Estimated Completion: 1.5-2 weeks @ 6 hrs/week