Build AI Observability Stack
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 7. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s): - Week 7: AI Observability and LLMOps (slug: ai-observability-107) 🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp. Deploy a complete observability stack for AI systems with Prometheus metrics, custom LLM exporters, and Grafana dashboards. Track token usage, latency, costs, and quality metrics.
Lab Overview
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 7. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):
- Week 7: AI Observability and LLMOps (slug: ai-observability-107)
🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.
Deploy a complete observability stack for AI systems with Prometheus metrics, custom LLM exporters, and Grafana dashboards. Track token usage, latency, costs, and quality metrics.
What You'll Learn
Deploy Prometheus and Grafana on Kubernetes
Create custom metrics exporters for LLM systems
Track token usage, latency, and cost metrics
Build Grafana dashboards for AI observability
Prerequisites
Kubernetes fundamentals
Understanding of Prometheus metrics
Basic Python programming
Technologies Covered
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