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LLMOps Engineering: Monitoring, Evaluation, and Production Lifecycle for AI Applications: The Complete Engineering Playbook for Deploying, Monitoring,
Paperback
Series: Production AI Engineering
Publisher: Independently Published
Published: Jun 4 2026
Pages: 92
Weight: 0.30
Height: 0.19 Width: 6.00 Depth: 9.00
Language: English
Going from a successful proof-of-concept to a reliable, production-grade Large Language Model (LLM) application is one of the hardest challenges in modern software engineering. LLMOps Engineering is your definitive, practical playbook to deploying, monitoring, and continuously improving LLMs at scale.
Written specifically for machine learning engineers, data scientists, and AI platform architects, this comprehensive guide bridges the gap between prompt engineering and enterprise-grade AI operations. You will learn how to build robust infrastructure that makes your non-deterministic AI systems observable, cost-effective, secure, and highly performant.
Inside this production-focused playbook, you will discover how to:
- Establish End-to-End Observability: Implement advanced tracing, logging, and metrics with Langfuse, Arize, and MLflow to capture rich LLM telemetry.
- Build Automated Evaluation Pipelines: Set up systematic evaluation using LLM-as-a-judge patterns, RAGAS, and DeepEval to measure correctness and bias.
- Optimize Infrastructure & Token Costs: Reduce latency and spend through semantic caching, prompt compression, and intelligent model routing techniques.
- Deploy Safely in Production: Run canary releases, shadow deployments, and complex A/B testing environments tailored for LLM outputs.
- Scale Production RAG Systems: Optimize vector databases, chunking strategies, and hybrid search for highly accurate contextual retrieval.
- Govern and Secure AI Applications: Navigate regulatory compliance, implement audit logging, and protect against prompt injection attacks.
Whether you are fine-tuning open-source models or integrating commercial APIs, this book provides the production-proven patterns, architecture diagrams, and tool references you need to run AI reliably. Stop guessing in production-engineer your LLM applications for scale.
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