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Duration 35 hours
Course Outline
Advanced LangGraph Structural Design
- Graph structural models: nodes, edges, routing logic, and subgraph integration
- State management: data channels, message exchange, and persistence strategies
- Directed acyclic graphs (DAG) versus cyclic workflows and layered composition
Performance Optimization and Tuning
- Parallel processing and concurrent execution patterns in Python
- Implementation of caching, data batching, tool invocation, and streaming capabilities
- Cost management protocols and token budget allocation strategies
Resilience and Reliability Engineering
- Recovery mechanisms: retry policies, timeout management, backoff algorithms, and circuit breaker patterns
- Ensuring idempotency and eliminating duplicate process steps
- State checkpointing and fault recovery utilizing local or cloud-based storage solutions
Diagnosis of Complex Graph Systems
- Execution tracing and preliminary dry-run simulations
- State verification and comprehensive event tracking
- Systematic reproduction of operational defects using defined seeds and test fixtures
System Observability and Continuous Monitoring
- Implementation of structured logging and distributed tracing capabilities
- Tracking of operational KPIs: response latency, system reliability, and token consumption
- Development of analytical dashboards, automated alerting systems, and service level objective (SLO) monitoring
Deployment Strategies and Operational Management
- Encapsulation of graph applications as standardized services and containerized units
- Governance of configuration parameters and secure handling of secrets
- Automation of CI/CD pipelines, staged rollouts, and canary deployment releases
Quality Assurance, Testing, and Security Posture
- Execution of unit tests, scenario simulations, and automated evaluation frameworks
- Establishment of safety guardrails, content filtering mechanisms, and PII protection protocols
- Adversarial testing (red teaming) and chaos engineering exercises to ensure system robustness
Conclusion and Future Implementation Steps
Requirements
- Proficiency in Python and asynchronous programming paradigms
- Practical experience in developing LLM-based applications
- Working knowledge of foundational LangGraph or LangChain concepts
Intended Audience
- AI platform engineers
- DevOps specialists focused on AI infrastructure
- ML architects responsible for managing production LangGraph systems