LLMs用於自動化客戶支持培訓

LangGraph 是一個用於構建有狀態、多參與者 LLM 應用的框架，它支持將應用構建爲具有持久狀態和執行控制的可組合圖。

本次由講師主導的培訓（線上或線下）面向高級 AI 平臺工程師、AI DevOps 和 ML 架構師，旨在幫助他們優化、調試、監控和操作生產級 LangGraph 系統。

通過本培訓，參與者將能夠：

• 設計和優化複雜的 LangGraph 拓撲結構，以提高速度、降低成本並增強可擴展性。
• 通過重試、超時、冪等性和基於檢查點的恢復機制來構建可靠性。
• 調試和追蹤圖執行過程，檢查狀態，並系統地復現生產問題。
• 爲圖添加日誌、指標和追蹤，部署到生產環境，並監控 SLA 和成本。

課程形式

• 互動式講座和討論。
• 大量練習和實踐。
• 在即時實驗室環境中進行實際操作。

課程定製選項

• 如需定製本課程，請聯繫我們安排。

Advanced Ollama Model Debugging & Evaluation is an in-depth course focused on diagnosing, testing, and measuring model behavior when running local or private Ollama deployments.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, ML Ops professionals, and QA practitioners who wish to ensure reliability, fidelity, and operational readiness of Ollama-based models in production.

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

• Perform systematic debugging of Ollama-hosted models and reproduce failure modes reliably.
• Design and execute robust evaluation pipelines with quantitative and qualitative metrics.
• Implement observability (logs, traces, metrics) to monitor model health and drift.
• Automate testing, validation, and regression checks integrated into CI/CD pipelines.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs and debugging exercises using Ollama deployments.
• Case studies, group troubleshooting sessions, and automation workshops.

Course Customization Options

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

這是一個由講師指導的線上或線下培訓，針對希望使用Ollama實現安全高效的AI驅動工作流程的高級專業人士。

通過本培訓，參與者將能夠：

• 部署和配置Ollama以進行私有AI處理。
• 將AI模型集成到安全的企業工作流程中。
• 在保持數據隱私的同時，優化AI性能。
• 利用本地AI功能自動化業務流程。
• 確保符合企業安全與治理政策。

這是一場由講師指導的現場培訓，地點在香港（線上或現場），適合希望使用Ollama部署、優化和整合LLM的中級專業人士。

在培訓結束時，參與者將能夠：

• 使用Ollama設置和部署LLM。
• 優化AI模型以提升性能和效率。
• 利用GPU加速提升推理速度。
• 將Ollama整合到工作流程和應用程式中。
• 監控和維護AI模型的長期性能。

這項由講師指導的香港（線上或線下）培訓，針對希望微調和自訂Ollama上的AI模型以提升性能和特定領域應用的高級專業人士。

在培訓結束時，參與者將能夠：

• 在Ollama上設置高效的AI模型微調環境。
• 準備用於監督式微調和強化學習的數據集。
• 優化AI模型的性能、準確性和效率。
• 在生產環境中部署自訂模型。
• 評估模型改進並確保其穩健性。

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 是一個用於構建圖結構 LLM 應用的框架，支持規劃、分支、工具使用、內存和可控執行。

本次由講師指導的培訓（線上或線下）面向初級開發者、提示工程師和數據從業者，旨在幫助他們使用 LangGraph 設計和構建可靠的多步驟 LLM 工作流。

培訓結束後，參與者將能夠：

• 解釋 LangGraph 的核心概念（節點、邊、狀態）及其使用場景。
• 構建支持分支、調用工具和保持內存的提示鏈。
• 將檢索和外部 API 集成到圖工作流中。
• 測試、調試和評估 LangGraph 應用的可靠性和安全性。

課程形式

• 互動講座和引導討論。
• 在沙盒環境中進行指導實驗和代碼演練。
• 基於場景的設計、測試和評估練習。

課程定製選項

• 如需爲此課程定製培訓，請聯繫我們安排。

LangGraph enables stateful, multi-actor workflows powered by LLMs with precise control over execution paths and state persistence. In healthcare, these capabilities are crucial for compliance, interoperability, and building decision-support systems that align with medical workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

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

• Design healthcare-specific LangGraph workflows with compliance and auditability in mind.
• Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
• Apply best practices for reliability, traceability, and explainability in sensitive environments.
• Deploy, monitor, and validate LangGraph applications in healthcare production settings.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with real-world case studies.
• Implementation practice 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 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 是一個基於圖的編排框架，支持條件化、多步驟的 LLM 和工具工作流，非常適合自動化和個性化內容管道。

本課程由講師主導，提供線上或線下培訓，面向中級營銷人員、內容策略師和自動化開發人員，旨在幫助他們使用 LangGraph 實現動態、分支的電子郵件營銷活動和內容生成管道。

在本培訓結束時，參與者將能夠：

• 設計具有條件邏輯的圖結構內容和電子郵件工作流。
• 集成 LLM、API 和數據源以實現自動化個性化。
• 在多步驟營銷活動中管理狀態、記憶和上下文。
• 評估、監控和優化工作流性能和交付結果。

課程形式

• 互動式講座和小組討論。
• 動手實驗，實現電子郵件工作流和內容管道。
• 基於場景的練習，涵蓋個性化、分段和分支邏輯。

課程定製選項

• 如需爲本課程定製培訓，請聯繫我們進行安排。

Ollama is a platform that enables running and fine-tuning large language and multimodal models locally.

This instructor-led, live training (online or onsite) is aimed at advanced-level ML engineers, AI researchers, and product developers who wish to build and deploy multimodal applications with Ollama.

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

• Set up and run multimodal models with Ollama.
• Integrate text, image, and audio inputs for real-world applications.
• Build document understanding and visual QA systems.
• Develop multimodal agents capable of reasoning across modalities.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice with real multimodal datasets.
• Live-lab implementation of multimodal pipelines using Ollama.

Course Customization Options

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

本課程為講師指導的香港（線上或線下）培訓，旨在幫助初級專業人士安裝、配置並使用Ollama在本地機器上運行AI模型。

培訓結束後，學員將能夠：

• 了解Ollama的基本原理及其功能。
• 設置Ollama以運行本地AI模型。
• 使用Ollama部署並與LLM進行互動。
• 優化AI工作負載的性能和資源使用。
• 探索本地AI部署在各行業中的應用案例。

Ollama is a platform for running large language and multimodal models locally and at scale.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level engineers who wish to scale Ollama deployments for multi-user, high-throughput, and cost-efficient environments.

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

• Configure Ollama for multi-user and distributed workloads.
• Optimize GPU and CPU resource allocation.
• Implement autoscaling, batching, and latency reduction strategies.
• Monitor and optimize infrastructure for performance and cost efficiency.

Format of the Course

• Interactive lecture and discussion.
• Hands-on deployment and scaling labs.
• Practical optimization exercises in live environments.

Course Customization Options

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

Ollama is a platform that enables running large language and multimodal models locally.

This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to master prompt engineering techniques to optimize Ollama outputs.

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

• Design effective prompts for diverse use cases.
• Apply techniques such as priming and chain-of-thought structuring.
• Implement prompt templates and context management strategies.
• Build multi-stage prompting pipelines for complex workflows.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with prompt design.
• Practical implementation in a live-lab environment.

Course Customization Options

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