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Course Outline
Advanced LangGraph Architecture
- Graph topology patterns, including nodes, edges, routers, and subgraphs
- State modeling through channels, message passing, and persistence
- Comparing DAGs with cyclic flows and exploring hierarchical composition
Performance and Optimization
- Implementing parallelism and concurrency patterns in Python
- Utilizing caching, batching, tool calling, and streaming
- Applying cost controls and token budgeting strategies
Reliability Engineering
- Configuring retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and deduplication of processing steps
- Checkpointing and recovery using local or cloud-based stores
Debugging Complex Graphs
- Conducting step-through execution 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 such as latency, reliability, and token usage
- Managing dashboards, alerts, and SLO tracking
Deployment and Operations
- Packaging graphs as services and containers
- Managing configuration and handling secrets
- Setting up CI/CD pipelines, rollouts, and canary releases
Quality, Testing, and Safety
- Developing unit, scenario, and automated eval harnesses
- Implementing guardrails, content filtering, and PII handling
- Performing red teaming and chaos experiments to test robustness
Summary and Next Steps
Requirements
- A solid grasp of Python and asynchronous programming principles
- Practical experience in developing LLM applications
- Basic familiarity with LangGraph or LangChain concepts
Target Audience
- AI platform engineers
- DevOps specialists focused on AI
- ML architects responsible for production LangGraph systems
35 Hours