n8n에서 AI 워크플로우 구축 교육 과정

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.

이 강사는 온라인 또는 현장에서 진행하는 실시간 교육과정은 Ollama을 사용하여 안전하고 효율적인 AI 기반 워크플로를 구현하고자 하는 고급 전문가들을 대상으로 합니다.

이 교육을 마친 후, 참가자들은 다음을 할 수 있습니다:

• Ollama을 사용하여 개인용 AI 처리 환경을 배포하고 구성합니다.
• AI 모델을 안전한 기업 워크플로우에 통합합니다.
• 데이터 프라이버시를 유지하면서 AI 성능을 최적화합니다.
• 온프레미스 AI 기능을 사용하여 비즈니스 프로세스를 자동화합니다.
• 기업 보안 및 관리에 준수합니다.

대한민국에서 강사가 진행하는 이 실시간 교육(온라인 또는 현장)은 Claude AI를 일상 업무에 통합하여 효율성과 자동화를 개선하려는 초보 수준의 전문가를 대상으로 합니다.

이 교육을 마치면 참가자는 다음을 수행할 수 있습니다.

• Claude AI를 사용하여 반복적인 작업을 자동화하고 워크플로를 간소화하세요.
• AI 기반 자동화를 활용해 개인 및 팀의 생산성을 향상하세요.
• Claude AI를 기존 비즈니스 도구와 플랫폼과 통합합니다.
• AI 기반 의사결정 및 업무 관리를 최적화하세요.

대한민국에서 강사가 진행하는 이 실시간 교육(온라인 또는 현장)은 Ollama를 사용하여 LLM을 배포, 최적화 및 통합하려는 중급 전문가를 대상으로 합니다.

이 교육을 마치면 참가자는 다음을 수행할 수 있습니다.

• Ollama를 사용하여 LLM을 설정하고 배포합니다.
• 성능과 효율성을 위해 AI 모델을 최적화합니다.
• 추론 속도를 개선하기 위해 GPU 가속을 활용하세요.
• Ollama를 워크플로 및 애플리케이션에 통합합니다.
• 시간 경과에 따라 AI 모델 성능을 모니터링하고 유지관리합니다.

이 강사는 온라인 또는 현장에서 진행되는 실습 위주의 교육으로, 조직 조직, 혈액 세포, 조류 및 기타 생물학적 샘플에 관련된 이미지를 처리하고 분석하고자 하는 초급자에서 중급자 수준의 연구자 및 실험실 전문가를 대상으로 합니다.

이 교육을 마치면 참가자들이 할 수 있는 것:

• Fiji 인터페이스를 탐색하고 ImageJ의 기본 기능을 활용합니다.
• 과학 이미지를 분석하기 위해 전처리하고 향상시킵니다.
• 세포 계산 및 면적 측정을 포함한 이미지를 정량적으로 분석합니다.
• 매크로와 플러그인을 사용하여 반복적인 작업을 자동화합니다.
• 생물학적 연구의 특정 이미지 분석 요구 사항에 맞게 워크플로를 사용자 정의합니다.

이 강사는 온라인 또는 현장에서 진행되는 실습형 교육으로, AI 모델의 성능을 최적화하고 도메인 특화 애플리케이션을 위해 Ollama에서 조정하고 사용자화하는 방법을 익히고자 하는 고급 수준의 전문가를 대상으로 합니다.

이 교육을 마친 후, 참가자는 다음과 같은 능력을 갖추게 됩니다:

• Ollama에서 AI 모델을 조정하기 위한 효율적인 환경을 설정합니다.
• 지도 학습 및 강화 학습을 위한 데이터셋을 준비합니다.
• AI 모델을 성능, 정확도 및 효율성을 위해 최적화합니다.
• 사용자화된 모델을 생산 환경에 배포합니다.
• 모델 개선 사항을 평가하고 강건성을 보장합니다.

대한민국에서 강사가 진행하는 이 실시간 교육(온라인 또는 현장)은 Claude AI의 기본을 이해하고 비즈니스 애플리케이션에 활용하려는 초보 수준의 비즈니스 전문가, 고객 지원 팀 및 기술 매니아를 대상으로 합니다.

이 교육을 마치면 참가자는 다음을 수행할 수 있습니다.

• Claude AI의 기능과 사용 사례를 알아보세요.
• Claude AI를 효과적으로 설정하고 상호작용해 보세요.
• 대화형 AI로 비즈니스 워크플로를 자동화하세요.
• AI 기반 솔루션을 사용하여 고객 참여와 지원을 강화하세요.

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.

이 강사는 온라인 또는 현장에서 초급 전문가를 위한 실시간 교육입니다. 이 교육은 로컬 머신에서 AI 모델을 실행하기 위해 Ollama을 설치, 구성 및 사용하는 방법을 학습하고자 하는 분들을 위한 것입니다.

이 교육을 마치면, 참가자는 다음을 할 수 있게 됩니다:

• Ollama의 기본 개념과 기능을 이해합니다.
• Ollama을 사용하여 로컬 AI 모델을 설정합니다.
• Ollama을 사용하여 LLMs를 배포하고 상호작용합니다.
• AI 워크로드에 대한 성능과 리소스 사용을 최적화합니다.
• 다양한 산업에서의 로컬 AI 배포 사례를 탐구합니다.