Module 4: Production Edge RAG at Scale
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.
View Diagram: Production Edge RAG Architecture
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
- Hands-On Lab → edge-rag-production-lab.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
- Start: Production Architecture
- Optimize: Performance Tuning
- Automate: MLOps Pipeline
- Hands-On: Lab
Module Duration: 10-12 hours
Estimated Completion: 1.5-2 weeks @ 6 hrs/week