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Duration 35 hours
Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs.
- State modeling techniques: channels, message passing, and persistence.
- Comparing DAGs versus cyclic flows and exploring hierarchical composition.
Performance and Optimization
- Implementing parallelism and concurrency patterns in Python.
- Leveraging caching, batching, tool calling, and streaming capabilities.
- Strategies for cost control and token budgeting.
Reliability Engineering
- Managing retries, timeouts, backoff mechanisms, and circuit breaking.
- Ensuring idempotency and deduplication of processing steps.
- Checkpointing and recovery strategies using local or cloud-based stores.
Debugging Complex Graphs
- Utilizing step-through execution and dry runs for analysis.
- Performing state inspection and event tracing.
- Reproducing production issues using seeds and fixtures.
Observability and Monitoring
- Implementing structured logging and distributed tracing.
- Tracking operational metrics: latency, reliability, and token usage.
- Configuring dashboards, alerts, and SLO tracking.
Deployment and Operations
- Packaging graphs as services and containers.
- Managing configuration and handling secrets securely.
- Establishing CI/CD pipelines, rollouts, and canary deployments.
Quality, Testing, and Safety
- Developing unit, scenario, and automated evaluation harnesses.
- Implementing guardrails, content filtering, and PII handling.
- Conducting red teaming and chaos experiments to ensure robustness.
Summary and Next Steps
Requirements
- A solid grasp of Python and asynchronous programming paradigms.
- Practical experience in developing LLM applications.
- Working knowledge of fundamental LangGraph or LangChain concepts.
Target Audience
- AI platform engineers.
- DevOps specialists for AI systems.
- ML architects responsible for production LangGraph deployments.
