Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP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.

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。

這個面向希望將Claude AI集成到他們的應用程序中、構建AI驅動的聊天機器人以及通過AI驅動的自動化來增強軟件功能的中級軟件開發人員和AI工程師的香港（在線或現場） Instructor-led, live training (online or onsite)。

在這次培訓結束時，參與者將能夠：

• 使用Claude AI API將AI集成到應用程序中。
• 開發AI驅動的聊天機器人和虛擬助手。
• 利用AI驅動的自動化和NLP增強應用程序。
• 對不同的用例優化和微調Claude AI模型。

這門由講師主導的現場培訓在香港（在線或現場）旨在幫助有志於將Claude AI整合到日常工作流程中以提高效率和自動化的初級專業人員。

培訓結束時，參加者將能夠：

• 利用Claude AI來自動化重複性任務和精簡工作流程。
• 利用人工智慧驅動的自動化技術提高個人和團隊的生產力。
• 將Claude AI與現有的商業工具和平台整合。
• 優化AI驅動的決策制定和任務管理。

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。

這項由講師主持的現場培訓（在線或現場）旨在幫助希望了解Claude AI的基本原理並利用它進行商業應用的初級商業專業人士、客戶支持團隊和技術愛好者。

培訓結束時，參與者將能夠：

• 了解Claude AI的能力和用例。
• 有效地設置和與Claude AI互動。
• 利用對話式AI自動化業務工作流程。
• 利用AI驅動的解決方案提升客戶互動和支持。

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 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 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.

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。