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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

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