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Duration 35 hours
Course Outline
Advanced LangGraph Architecture
- Graph topology patterns including nodes, edges, routers, and subgraphs.
- State modeling through channels, message passing, and persistence strategies.
- Differences between DAG and cyclic flows, along with hierarchical composition.
Performance and Optimization
- Parallelism and concurrency patterns within the Python ecosystem.
- Techniques for caching, batching, tool calling, and streaming.
- Strategies for cost control and effective token budgeting.
Reliability Engineering
- Implementation of retries, timeouts, backoff mechanisms, and circuit breaking.
- Ensuring idempotency and deduplication of processing steps.
- Checkpointing and recovery procedures using local or cloud-based stores.
Debugging Complex Graphs
- Utilizing step-through execution and dry runs for analysis.
- Inspecting states and tracing events for detailed diagnostics.
- Reproducing production issues using seeds and fixtures.
Observability and Monitoring
- Adopting structured logging and distributed tracing practices.
- Tracking operational metrics such as latency, reliability, and token usage.
- Setting up dashboards, alerts, and SLO tracking mechanisms.
Deployment and Operations
- Packaging graphs as scalable services and containers.
- Managing configurations and handling secrets securely.
- Integrating CI/CD pipelines, rollouts, and canary deployments.
Quality, Testing, and Safety
- Developing unit tests, scenario tests, and automated evaluation harnesses.
- Implementing guardrails, content filtering, and PII handling protocols.
- Conducting red teaming and chaos experiments to ensure robustness.
Summary and Next Steps
Requirements
- Proficiency in Python and asynchronous programming concepts.
- Practical experience in developing LLM-based applications.
- Familiarity with foundational LangGraph or LangChain principles.
Target Audience
- AI platform engineers.
- DevOps professionals specializing in AI.
- ML architects responsible for managing production LangGraph systems.
