LangGraph in Healthcare: Workflow Orchestration for Regulated Environments 培训

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI platform engineers, DevOps for AI, and ML architects who wish to optimize, debug, monitor, and operate production-grade LangGraph systems.

By the end of this training, participants will be able to:

• Design and optimize complex LangGraph topologies for speed, cost, and scalability.
• Engineer reliability with retries, timeouts, idempotency, and checkpoint-based recovery.
• Debug and trace graph executions, inspect state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy to production, and monitor SLAs and costs.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

这门由讲师主导的现场培训课程（线上或线下）旨在让中级到高级的医疗保健专业人员和希望实施AI驱动的医疗保健解决方案的AI开发人员参加。

培训结束时，参与者将能够：

• 了解AI代理在医疗保健和诊断中的作用。
• 开发医疗图像分析和预测诊断的AI模型。
• 将AI与电子健康记录（EHR）和临床工作流程集成。
• 确保遵守医疗保健法规和伦理AI实践。

本次由讲师指导的中国（线上或线下）培训面向中级医疗保健专业人员，旨在帮助他们应用AI和AR/VR技术于医疗培训、手术模拟和康复领域。

培训结束后，参与者将能够：

• 了解AI在增强医疗AR/VR体验中的作用。
• 使用AR/VR进行手术模拟和医疗培训。
• 在患者康复和治疗中应用AR/VR工具。
• 探讨AI增强医疗工具中的伦理和隐私问题。

本课程为讲师指导的中国（线上或线下）培训，面向中级数据科学家和医疗专业人士，旨在帮助他们利用Google Colab在高级医疗应用中应用人工智能。

通过本培训，学员将能够：

• 使用Google Colab实现医疗人工智能模型。
• 利用人工智能进行医疗数据的预测建模。
• 使用人工智能技术分析医学影像。
• 探讨基于人工智能的医疗解决方案中的伦理问题。

这是一个由讲师指导的现场培训课程，地点在中国（线上或现场），针对希望了解并在医疗环境中应用AI技术的中级医疗专业人员和数据科学家。

在本培训结束时，参与者将能够：

• 识别AI可以解决的关键医疗挑战。
• 分析AI对患者护理、安全和医学研究的影响。
• 理解AI与医疗商业模式之间的关系。
• 将基本的AI概念应用于医疗情境。
• 开发用于医学数据分析的机器学习模型。

本次由讲师指导的中国（线上或线下）培训，旨在帮助医疗保健专业人士和研究人员利用ChatGPT来提升患者护理、优化工作流程，并改善医疗保健成果。

培训结束后，参与者将能够：

• 理解ChatGPT的基本原理及其在医疗保健中的应用。
• 利用ChatGPT自动化医疗保健流程和互动。
• 使用ChatGPT为患者提供准确的医疗信息和支持。
• 应用ChatGPT进行医学研究和分析。

这种以讲师为主导的中国（在线或现场）现场培训面向希望利用边缘 AI 提供创新医疗保健解决方案的中级医疗保健专业人员、生物医学工程师和 AI 开发人员。

在培训结束时，参与者将能够：

• 了解边缘 AI 在医疗保健中的作用和优势。
• 在医疗保健应用的边缘设备上开发和部署 AI 模型。
• 在可穿戴设备和诊断工具中实施边缘 AI 解决方案。
• 使用边缘 AI 设计和部署患者监护系统。
• 解决医疗保健 AI 应用程序中的道德和监管考虑因素。

这种由讲师指导的中国（在线或现场）现场培训面向希望在医疗保健环境中了解和应用生成式人工智能的初级到中级医疗保健专业人员、数据分析师和政策制定者。

在培训结束时，参与者将能够：

• 解释生成式 AI 在医疗保健领域的原理和应用。
• 确定生成式 AI 增强药物发现和个性化医疗的机会。
• 利用生成式 AI 技术进行医学成像和诊断。
• 评估人工智能在医疗环境中的伦理影响。
• 制定将人工智能技术整合到医疗保健系统中的战略。

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based finance solutions with proper governance, observability, and compliance.

By the end of this training, participants will be able to:

• Design finance-specific LangGraph workflows aligned to regulatory and audit requirements.
• Integrate financial data standards and ontologies into graph state and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems for performance, cost, and SLAs.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework for building graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.

By the end of this training, participants will be able to:

• Explain core LangGraph concepts (nodes, edges, state) and when to use them.
• Build prompt chains that branch, call tools, and maintain memory.
• Integrate retrieval and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph apps for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises on design, testing, and evaluation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and precise control over execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.

By the end of this training, participants will be able to:

• Design legal-specific LangGraph workflows that preserve auditability and compliance.
• Integrate legal ontologies and document standards into graph state and processing.
• Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
• Deploy, monitor, and maintain LangGraph services in production with observability and cost controls.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.

By the end of this training, participants will be able to:

• Design graph-based workflows that coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallbacks for robust execution.
• Integrate retrieval, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behavior for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based design exercises and peer reviews.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a graph-based orchestration framework that enables conditional, multi-step LLM and tool workflows, ideal for automating and personalizing content pipelines.

This instructor-led, live training (online or onsite) is aimed at intermediate-level marketers, content strategists, and automation developers who wish to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.

By the end of this training, participants will be able to:

• Design graph-structured content and email workflows with conditional logic.
• Integrate LLMs, APIs, and data sources for automated personalization.
• Manage state, memory, and context across multi-step campaigns.
• Evaluate, monitor, and optimize workflow performance and delivery outcomes.

Format of the Course

• Interactive lectures and group discussions.
• Hands-on labs implementing email workflows and content pipelines.
• Scenario-based exercises on personalization, segmentation, and branching logic.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

这个在中国 (线上或线下) 进行的由讲师主导的现场培训旨在让中级到高级的医疗保健专业人员、医学研究人员和希望在医疗诊断和医疗保健应用中应用多模态人工智能的人工智能开发人员参加。

培训结束时，参与者将能够：

• 了解多模态人工智能在现代医疗保健中的作用。
• 整合用于人工智能驱动诊断的结构化和非结构化医疗数据。
• 应用人工智能技术分析医疗影像和电子健康纪录。
• 开发疾病诊断和治疗建议的预测模型。
• 实施语音和自然语言处理 (NLP) 以进行医疗笔记和病人互动。

此面向中级医疗保健专业人员和希望利用提示工程技术改善医疗工作流程、研究效率和患者结果的 AI 开发人员的以讲师为主导的现场培训旨在满足他们的需求。此培训在 中国（在线或现场）进行。 培训结束时，参与者将能够：

• 了解医疗保健领域提示工程的基础知识。
• 在临床文档和患者互动中使用 AI 提示。
• 利用 AI 进行医学研究和文献审查。
• 通过 AI 驱动的提示增强药物发现和临床决策。
• 确保医疗保健 AI 中的监管和伦理标准的合规性。