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Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs.
- State modeling: channels, message passing, and persistence.
- Understanding DAG versus cyclic flows and hierarchical composition.
Performance and Optimization
- Parallelism and concurrency patterns in Python.
- Techniques for caching, batching, tool calling, and streaming.
- Strategies for cost controls and token budgeting.
Reliability Engineering
- Implementing retries, timeouts, backoff strategies, and circuit breaking.
- Achieving idempotency and deduplicating steps.
- Utilizing checkpointing and recovery mechanisms with local or cloud storage.
Debugging Complex Graphs
- Conducting step-through execution and dry runs.
- 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 such as latency, reliability, and token usage.
- Setting up dashboards, alerts, and SLO tracking.
Deployment and Operations
- Packaging graphs as services and containers.
- Managing configurations and handling secrets.
- Establishing CI/CD pipelines, rollouts, and canary deployments.
Quality, Testing, and Safety
- Utilizing unit tests, scenario-based testing, 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 understanding of Python and asynchronous programming.
- Practical experience with LLM application development.
- Familiarity with foundational LangGraph or LangChain concepts.
Target Audience
- AI platform engineers.
- DevOps professionals focused on AI.
- ML architects responsible for managing production LangGraph systems.
35 Hours
