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Duration 35 hours
Course Outline
Advanced LangGraph Architecture
- Exploring graph topology patterns: nodes, edges, routers, and subgraphs
- State modeling techniques: channels, message passing, and persistence
- Comparing DAGs versus cyclic flows and understanding hierarchical composition
Performance and Optimization
- Applying parallelism and concurrency patterns in Python
- Leveraging caching, batching, tool calling, and streaming
- Implementing cost controls and token budgeting strategies
Reliability Engineering
- Managing retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and deduplication of steps
- Checkpointing and recovery using local or cloud-based stores
Debugging Complex Graphs
- Executing step-through debugging and dry runs
- Inspecting state and tracing events
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Implementing structured logging and distributed tracing
- Tracking operational metrics: latency, reliability, and token usage
- Building dashboards, setting alerts, and monitoring SLOs
Deployment and Operations
- Packaging graphs as services and containers
- Handling configuration management and secrets
- Integrating CI/CD pipelines, rollouts, and canary releases
Quality, Testing, and Safety
- Developing unit, scenario, and automated evaluation harnesses
- Implementing guardrails, content filtering, and PII handling
- Conducting red teaming and chaos experiments for robustness
Summary and Next Steps
Requirements
- Solid understanding of Python and asynchronous programming.
- Practical experience in developing LLM applications.
- Basic familiarity with LangGraph or LangChain concepts.
Target Audience
- AI Platform Engineers
- DevOps Specialists for AI
- ML Architects responsible for production LangGraph systems
