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LLMOps: Production Engineering for the AI
Week 1: LLMOps Foundations
LLMOps vs traditional MLOps: Key differences
Software engineering for LLMs and agentic systems
Project organization for AI agent development
Saturday Lab: Building a structured LLM application
Week 2: Data Engineering for Foundation Models
Data processing for LLMs and multi-modal models
Vector databases and embedding management
Version control for RAG knowledge bases
Week 3: LLM Development Lifecycle
The unique lifecycle of foundation model applications
Fine-tuning and adaptation workflows
Validation frameworks for LLMs and agentic systems
Saturday Lab: Creating reproducible LLM fine-tuning pipelines
Week 4: CI/CD for LLM Applications
Continuous integration for LLM code and prompts
Continuous delivery of AI agent systems
Testing strategies for agentic AI
Saturday Lab: Building end-to-end LLMOps pipelines
Week 5: Deployment Strategies
Deployment patterns for LLMs and AI agents
API design for LLM services
Scalable RAG system architecture
Saturday Lab: Implementing scalable RAG infrastructure
Week 6: Monitoring & Continuous Improvement
LLM-specific performance monitoring
Detecting drift in foundation model systems
Continuous improvement of agentic AI
Saturday Lab: Building LLM monitoring systems
Week 7: Security and Advanced Topics
Security and privacy for LLM applications
Advanced topics: Multi-agent systems
Emerging LLMOps frameworks and tools
Saturday Lab: Capstone project design
Week 8: Capstone Project
Implementation of agentic RAG system
Multi-modal AI integration
Testing and deployment
Saturday: Final presentation and certification
Detecting drift in foundation model systems
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