Publication Specifications
FORMATDRM-Free PDF
LENGTH42 Pages
TIERadvanced Edition
PUBLISHERBlizarra Ltd
Advanced42 pages • Instant Download
Production LLMOps & Observability
Distributed tracing, evals, latency optimization, cache hit rates, and cost governance at scale.
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About this Book
Deploying an LLM is easy; operating it for millions of users without bankrupting your company or suffering silent regressions is hard. This book covers OpenTelemetry tracing, prompt versioning, semantic caching, automated CI/CD eval gates, and multi-provider failover routing.
Target Audience: DevOps, SRE, and platform engineers scaling AI workloads in high-traffic environments.
What You Will Learn & Build
Track full generation traces, token counts, and step latencies with OpenTelemetry
Implement semantic caching with Redis and Qdrant to cut API spend by 30-60%
Create automated evaluation test suites that block broken prompts in CI/CD
Build dynamic load balancers with automatic fallback to secondary model providers
Table of Contents
1. Anatomy of an LLMOps ArchitectureSection 1
2. Distributed Tracing: Spans, Metadata & Parent-Child RequestsSection 2
3. Semantic Caching: Thresholds, Distance Metrics & InvalidationSection 3
4. Designing Continuous Evals in GitHub ActionsSection 4
5. Rate Limiting, Token Buckets & Graceful DegradationSection 5
6. Multi-Provider Fallbacks (OpenAI, Anthropic, Gemini, DeepSeek)Section 6
7. Cost Governance & Token Budget AttributionSection 7
8. Incident Response & On-Call Runbooks for LLM SystemsSection 8