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1
- Introduction
2
- AI Market Observations
3
- AI Adoption Challenges
4
- AI Observability Overview
5
- Demo Architecture
6
- Demo begins
7
- Analyzing Slow AI Requests
8
- Analyzing Costs of AI Usage
9
- Analyzing the Performance of Models
10
- Analyzing AI Accuracy
11
- Wrap Up
Description:
Dive into a 15-minute tutorial on AI and LLM Observability using Dynatrace's OpenTelemetry-based solution. Explore the challenges companies face when adopting AI and understand the benefits of observing AI and LLM. Watch a comprehensive demo analyzing typical AI challenges, including slow requests, performance issues, and response accuracy. Learn how to optimize performance, understand costs, and ensure accurate responses across all layers of the AI Stack: Infrastructure, Model, Semantic, Orchestration, and Application. Access sample dashboards, deploy a GenAI demo app using GitHub Codespace, and implement AI observability in your environments with detailed documentation. The tutorial covers AI market observations, adoption challenges, observability overview, demo architecture, and in-depth analysis of slow AI requests, usage costs, model performance, and AI accuracy.

AI and LLM Observability with Dynatrace - Tutorial and Demo

Dynatrace
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