Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs.
- State modeling: channels, message passing, and persistence.
- DAG versus cyclic flows and hierarchical composition.
Performance and Optimization
- Parallelism and concurrency patterns in Python.
- Caching, batching, tool calling, and streaming.
- Cost controls and token budgeting strategies.
Reliability Engineering
- Retries, timeouts, backoff strategies, and circuit breaking.
- Idempotency and deduplication of steps.
- Checkpointing and recovery using local or cloud stores.
Debugging Complex Graphs
- Step-through execution and dry runs.
- State inspection and event tracing.
- Reproducing production issues using seeds and fixtures.
Observability and Monitoring
- Structured logging and distributed tracing.
- Operational metrics: latency, reliability, and token usage.
- Dashboards, alerts, and SLO tracking.
Deployment and Operations
- Packaging graphs as services and containers.
- Configuration management and secrets handling.
- CI/CD pipelines, rollouts, and canary deployments.
Quality, Testing, and Safety
- Unit testing, scenario testing, and automated eval harnesses.
- Guardrails, content filtering, and PII handling.
- Red teaming and chaos experiments for robustness.
Summary and Next Steps
Requirements
- A solid understanding of Python and asynchronous programming.
- Experience in developing LLM applications.
- Familiarity with core LangGraph or LangChain concepts.
Audience
- AI platform engineers.
- DevOps specialists for AI.
- ML architects responsible for production LangGraph systems.
35 Hours
