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llm observability: observability maturity model for aws bedrock integrated applications
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Classroom Contents
LLMs and AWS: Observability Maturity from Foundation to AIOps
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- 1 intro
- 2 preamble
- 3 agenda
- 4 quick intro about myself
- 5 amazon bedrock
- 6 overview of genai apps
- 7 what is observability?
- 8 observability in llms: what exactly is it?
- 9 insight into the core: direct llm observability
- 10 beyond the surface: indirect llm observability
- 11 llm observability: observability maturity model for aws bedrock integrated applications
- 12 why observability matters for llms
- 13 pillars shaping indirect llm observability
- 14 llm specific metrics
- 15 prompt engineering properties
- 16 performance metrics, logging, and tracing for rag models
- 17 tracing
- 18 visualization tools
- 19 alerting and incident management
- 20 security and compliance
- 21 cost optimization
- 22 aiops capabilities
- 23 why you need maturity model?
- 24 maturity framework
- 25 level 1: foundation llm observability
- 26 measure progress with business outcomes
- 27 best practices
- 28 pitfalls to avoid
- 29 thank you
