LangGraph in Healthcare: Workflow Orchestration for Regulated Environments培訓

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.

這門由講師主導的現場培訓課程（線上或線下）旨在讓中級到高級的醫療保健專業人員和希望實施AI驅動的醫療保健解決方案的AI開發人員參加。

培訓結束時，參與者將能夠：

• 了解AI代理在醫療保健和診斷中的作用。
• 開發醫療圖像分析和預測診斷的AI模型。
• 將AI與電子健康記錄（EHR）和臨床工作流程集成。
• 確保遵守醫療保健法規和倫理AI實踐。

本次由讲师指导的台灣（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。

本次由讲师指导的台灣（线上或线下）培训，面向中级数据科学家和医疗保健专业人士，旨在帮助他们利用Google Colab进行高级医疗保健应用的人工智能技术。

通过本次培训，参与者将能够：

• 使用Google Colab实现医疗保健领域的AI模型。
• 利用AI进行医疗数据的预测建模。
• 使用AI驱动的技术分析医学图像。
• 探讨基于AI的医疗解决方案中的伦理问题。

這是一個由講師指導的現場培訓課程，地點在台灣（線上或現場），針對希望了解並在醫療環境中應用AI技術的中級醫療專業人員和數據科學家。

在本培訓結束時，參與者將能夠：

• 識別AI可以解決的關鍵醫療挑戰。
• 分析AI對患者護理、安全和醫學研究的影響。
• 理解AI與醫療商業模式之間的關係。
• 將基本的AI概念應用於醫療情境。
• 開發用於醫學數據分析的機器學習模型。

本次由讲师指导的台灣（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。

這種以講師為主導的台灣（在線或現場）現場培訓面向希望利用邊緣 AI 提供創新醫療保健解決方案的中級醫療保健專業人員、生物醫學工程師和 AI 開發人員。

在培訓結束時，參與者將能夠：

• 了解邊緣 AI 在醫療保健中的作用和優勢。
• 在醫療保健應用的邊緣設備上開發和部署 AI 模型。
• 在可穿戴設備和診斷工具中實施邊緣 AI 解決方案。
• 使用邊緣 AI 設計和部署患者監護系統。
• 解決醫療保健 AI 應用程式中的道德和監管考慮因素。

這種由講師指導的台灣（在線或現場）現場培訓面向希望在醫療保健環境中瞭解和應用生成式人工智慧的初級到中級醫療保健專業人員、數據分析師和政策制定者。

在培訓結束時，參與者將能夠：

• 解釋生成式 AI 在醫療保健領域的原理和應用。
• 確定生成式 AI 增強藥物發現和個人化醫療的機會。
• 利用生成式 AI 技術進行醫學成像和診斷。
• 評估人工智慧在醫療環境中的倫理影響。
• 制定將人工智慧技術整合到醫療保健系統中的戰略。

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.

這個在台灣 (線上或線下) 進行的由講師主導的現場培訓旨在讓中級到高級的醫療保健專業人員、醫學研究人員和希望在醫療診斷和醫療保健應用中應用多模態人工智能的人工智能開發人員參加。

培訓結束時，參與者將能夠：

• 了解多模態人工智能在現代醫療保健中的作用。
• 整合用於人工智能驅動診斷的結構化和非結構化醫療數據。
• 應用人工智能技術分析醫療影像和電子健康紀錄。
• 開發疾病診斷和治療建議的預測模型。
• 實施語音和自然語言處理 (NLP) 以進行醫療筆記和病人互動。

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 台灣（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。