LLMs in Production: Real-World Strategies for LLMs Deployment, Monitoring, and Optimization

By Husn Ara

LLMs in Production: Real-World Strategies for LLMs Deployment, Monitoring, and Optimization

Unlock the full potential of Large Language Models (LLMs) with this hands-on guide to designing, deploying, and managing LLM-powered applications at scale. Whether you're a machine learning engineer, software developer, data scientist, or tech leader, LLMs in Production provides everything you need to move from proof-of-concept to production-grade AI systems.

Covering the entire lifecycle of production-ready LLMs, this book dives into real-world best practices for inference optimization, model serving, latency reduction, GPU utilization, multi-modal deployment, fine-tuning, prompt engineering, observability, failure recovery, and more.

Inside You'll Learn: LLM Fundamentals: Understand tokenization, attention mechanisms, decoding strategies, and model behavior.Model Serving: Compare open-source LLM serving stacks (Triton, vLLM, TGI, Ray Serve), and deploy them with FastAPI, Kubernetes, or serverless tools.Scaling Inference: Manage cost, speed, and throughput using quantization, batch serving, multi-GPU inference, and caching.Fine-Tuning & Instruction Tuning: Use LoRA, QLoRA, and domain-specific datasets to improve performance without massive retraining costs.Multi-Modal Interfaces: Integrate LLMs with vision (CLIP, LLaVA), audio (Whisper), and tools (RAG, function calling).Testing & Evaluation: Automate prompt evaluation, hallucination detection, and alignment checks in CI/CD pipelines.Production Guardrails: Add safety layers, moderation filters, and sandboxed tool use with langchain, guardrails.ai, or custom logic.Monitoring & Observability: Track latency, usage patterns, quality drift, and model health with Prometheus, OpenTelemetry, and custom logs.Retrieval-Augmented Generation (RAG): Build scalable RAG pipelines with vector databases like FAISS, Weaviate, and Elasticsearch.
Whether you're deploying an enterprise chatbot, summarization engine, code assistant, or a fully autonomous AI agent, LLMs in Production equips you with the knowledge and tools to deliver performant, scalable, and safe AI products.

Who This Book Is For: AI/ML engineers deploying LLMs in production environmentsBackend engineers integrating model inference into applicationsMLOps professionals managing model infrastructure and observabilityProduct teams building real-time, generative AI user experiences

• Format: Paperback
• Language: English
• ISBN: B0FK8V47C5
• ISBN13: 9798294490713
• Release Date: July 2025
• Publisher: Independently Published
• Length: 220 Pages
• Weight: 0.66 lbs.
• Dimensions: 0.5" x 6.0" x 9.0"