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Course Outline
LangGraph Fundamentals for Finance
- Refresher on LangGraph architecture and stateful execution.
- Finance use cases: research copilots, trade support, customer service agents.
- Regulatory constraints and auditability considerations.
Financial Data Standards and Ontologies
- ISO 20022, FpML, and FIX basics.
- Mapping schemas and ontologies into graph state.
- Data quality, lineage, and PII handling.
Workflow Orchestration for Financial Processes
- KYC and AML onboarding workflows.
- Trade lifecycle, exceptions, and case management.
- Credit adjudication and decisioning paths.
Compliance, Risk, and Controls
- Policy enforcement and model risk management.
- Guardrails, approvals, and human-in-the-loop steps.
- Audit trails, retention, and explainability.
Integration and Deployment
- Connecting to core systems, data lakes, and APIs.
- Containerization, secrets, and environment management.
- CI/CD pipelines, staged rollouts, and canaries.
Observability and Performance
- Structured logs, metrics, traces, and cost monitoring.
- Load testing, SLOs, and error budgets.
- Incident response, rollback, and resilience patterns.
Quality, Evaluation, and Safety
- Unit, scenario, and automated eval harnesses.
- Red teaming, adversarial prompts, and safety checks.
- Dataset curation, drift monitoring, and continuous improvement.
Summary and Next Steps
Requirements
- An understanding of Python and LLM application development
- Experience with APIs, containers, or cloud services
- Basic familiarity with financial domains or data models
Audience
- Domain technologists
- Solution architects
- Consultants building LLM agents in regulated industries
35 Hours
회원 평가 (1)
제가 매우 감사한 점은 강사가 모든 내용을 얼마나 잘 설명했는지입니다. Finance이 제 전공 분야는 아니지만, 모든 참가자가 동일한 페이지에 있도록 했으며, 남은 시간을 유지했습니다. 연습은 적절한 간격에 배치되었습니다. Communication는 참가자들과의 소통은 항상 있었습니다. 자료는 너무 많지도, 적지도 않았습니다. 그는 복잡한 주제를 더 잘 설명하여 모든 사람이 이해할 수 있도록 했습니다.
Diana
Course - ChatGPT for Finance
Machine Translated
