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0:00 - Introduction: Why AI Observability Matters Now
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Classroom Contents
Why Observability Matters More with AI Applications
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- 1 0:00 - Introduction: Why AI Observability Matters Now
- 2 1:15 - Live Demo Preview: RAG with Llama Stack & Safety Features
- 3 4:30 - The State of AI in Enterprise: Moving from Research to Business-Critical
- 4 6:55 - Unique Monitoring Challenges Posed by LLMs
- 5 9:15 - Prefill vs. Decode: The Core Difference in LLM Serving Patterns
- 6 12:05 - Building the Open-Source Stack: Prometheus, Grafana, Tempo, and OTel
- 7 15:00 - Kubernetes Deep Dive: ServiceMonitors Explained
- 8 18:45 - Deploying the Model: Using llm-d for vLLM Quick Start
- 9 22:10 - Configuring Tracing with Llama Stack and OTel Sidecars
- 10 27:50 - Critical Signals to Monitor: Performance, Cost, and Quality
- 11 32:00 - Live Demo: Analyzing GPU Usage, vLLM Dashboards & Traces in Grafana
- 12 37:45 - Q&A: Open-Source Cost, Langfuse, and Actionable Analytics for Different Personas
