Explore how Dynatrace provides OpenTelemetry-based AI and LLM Observability into all layers of your AI Stack: Infrastructure, Model, Semantic, Orchestration, and Application.
Organizations adopting Artificial Intelligence (AI) and large language models (LLMs) struggle to optimize performance, understand costs, and ensure accurate responses.
In this use case tutorial, you'll:
🚀 Learn what challenges companies face when adopting AI
🚀 Understand how to observe AI and LLM and the benefits of doing so
🚀 Watch a full demo on observing and analyzing typical AI challenges, including slow requests, performance issues, and accurate responses.
-----------------------------
📖 Chapters 📖
00:00 - Introduction
00:26 - AI Market Observations
00:54 - AI Adoption Challenges
01:26 - AI Observability Overview
03:34 - Demo Architecture
05:00 - Demo begins
05:53 - Analyzing Slow AI Requests
09:22 - Analyzing Costs of AI Usage
10:48 - Analyzing the Performance of Models
11:30 - Analyzing AI Accuracy
13:40 - Wrap Up
-----------------------------
🔗 Additional Links
Try our platform with a free trial, or schedule a personalized demo
Explore our sample dashboards on the Dynatrace Playground
Get hands-on and deploy our GenAI demo app using GitHub Codespace
Implement AI observability in your environments with our detailed Dynatrace Documentation
-----------------------------
Subscribe to our YT channel
Stay up-to-date with Dynatrace! Follow us on Facebook, Instagram, LinkedIn, Twitter, Twitch
