Lab: Production Edge RAG Deployment

🚧 Lab Under Development
This lab content is complete but hands-on exercises are currently being validated and refined.
Expected Release: Q1 2026
You can review the lab steps and prepare your environment in advance.

Lab Scenario

Company: IntelliData Systems
Objective: Deploy production RAG system on edge

Learning Outcomes

  • Deploy production RAG architecture
  • Optimize retrieval and inference
  • Implement MLOps pipeline
  • Monitor system performance
  • Execute model updates

Exercises

Exercise 1: Architecture Setup (120 minutes)

  1. Deploy ingestion pipeline
  2. Configure vector database
  3. Set up inference engines
  4. Validate component integration
  5. Test end-to-end RAG flow

Exercise 2: Optimization (90 minutes)

  1. Collect baseline metrics
  2. Apply optimization techniques
  3. Measure improvements
  4. Document trade-offs
  5. Establish production settings

Exercise 3: MLOps Implementation (120 minutes)

  1. Set up model versioning
  2. Create CI/CD pipeline
  3. Implement monitoring
  4. Configure retraining triggers
  5. Test update procedures

Exercise 4: Performance Testing (60 minutes)

  1. Conduct load testing
  2. Measure latency
  3. Monitor resource usage
  4. Identify bottlenecks
  5. Optimize configuration
See also: Architecture Optimization MLOps