[! 중요] Collaborators는 추가되지 않으며 워크샵 토큰은 활성 워크샵에 영향을 미치는 영향을 피하기 위해 회전되지 않습니다. 현재 작업장 토큰을 얻기 위해 내부 팀에 문의하십시오.

Dynatrace와 Model Context Protocol(MCP)을 통해 AI/LLM Observability 학습을 위한 핸드 온 워크샵.

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#🚀 워크샵 출석: 여기 시작! · #####의 · 가이드는 Codespace를 실행하고 환경을 구성하고, 상세한 지침, 코드 스니펫 및 스크린 샷으로 모든 실험실을 완료합니다.

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워크샵 개요

인포메이션 인포메이션 |--------|---------| | 레벨 | 중급 | |Format | 핸드 온 랩 | | **플랫폼 ** | GitHub Codespace |

당신이 배울 것

• ✅ OpenLLMetry를 가진 계기 AI/LLM 신청
• ✅ OTLP를 통해 Dynatrace에 추적 보내기
• ✅ 분석 LLM 성능, 토큰 사용 및 비용
• ✅ 에이전트 AI 워크플로우를 위한 Dynatrace MCP 사용
• ✅ Dynatrace Workflow와 AI 비용 모니터링

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🎯 Dynatrace AI Observability & MCP Workshop

[!IMPORTANT]
Collaborators are not being added, and the workshop token is not being rotated to avoid impacting active workshops. Please contact the internal team to obtain the current workshop token.

A hands-on workshop for learning AI/LLM observability with Dynatrace and the Model Context Protocol (MCP).

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🚀 Workshop Attendees: Start Here!

The guide walks you through launching your Codespace, configuring your environment, and completing all labs with detailed instructions, code snippets, and screenshots.

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📋 Workshop Overview

What You’ll Learn

• ✅ Instrument AI/LLM applications with OpenLLMetry
• ✅ Send traces to Dynatrace via OTLP
• ✅ Analyze LLM performance, token usage, and costs
• ✅ Use Dynatrace MCP for agentic AI workflows
• ✅ Automate AI cost monitoring with Dynatrace Workflows

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📚 Workshop Labs

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👨‍🏫 Instructor Setup

How It Works

Attendees share the same repository—they don’t fork it. Each attendee launches their own isolated Codespace, and all their code modifications stay private to that Codespace session.

The workshop uses a pre-deployed Azure Function secrets server to securely distribute Azure OpenAI credentials. Attendees enter a workshop token—they never see the actual API keys.

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🛤️ Choose Your Setup Path

There are two ways to run this workshop as an instructor:

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Option A: Use Existing Infrastructure (Recommended)