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Production-Ready LLMs: Build, Deploy and Scale Real AI Systems with Python, RAG, and Agents
Paperback
Publisher: Independently Published
Published: Nov 30 2025
Pages: 446
Weight: 2.26
Height: 0.90 Width: 8.50 Depth: 11.00
Language: English
If you've ever tried to build an AI product and quickly realized that tutorials don't prepare you for real production systems, you're not alone. Most books stop at the basics-while the real challenges lie in deploying scalable LLM systems, optimizing costs, handling errors in production, and building applications that actually work for real users.
This book is for engineers, founders, data scientists, and developers who want to go beyond toy demos and finally learn how to build production-ready LLMs, deploy enterprise-grade RAG architecture, and scale real AI systems using Python, agents, and vector databases. If you're exploring RAG systems, multimodal LLMs, agentic AI engineering, or model serving infrastructure, this book gives you the complete blueprint.
You will learn how to build LLM applications and agent systems that deploy reliably in the cloud, scale to thousands of users, reduce operational costs, and use modern best practices such as quantization, vector search, RAG with vector database integration, cost optimization, and routing between small and large models. Whether you're focused on LLM fine-tuning with Python, serving LLM models in production, or architecting hybrid environments that support API, containerization, and GPU workloads, this book shows you exactly how to implement it step-by-step.
Inside, you'll discover how to:Build RAG systems in Python that outperform generic chatbot architectures.
Deploy scalable LLM systems using best practices in cloud, GPU scheduling, and routing.
Implement agentic AI engineering patterns that work in real production environments.
Integrate vector search, embeddings, and RAG architecture design for enterprise use.
Apply FinOps and cost optimization strategies-including quantization, batching, and caching.
Build and deploy agent systems using modern stacks like vLLM, LangChain, and LangGraph.
Design fault-tolerant pipelines for LLM infrastructure and deployment at scale.
Master real-world workflows for model serving, evaluation, and monitoring.
Whether you're building internal tools, a high-volume SaaS product, or an AI-driven platform, this book gives you the exact blueprint to design and deploy systems that are reliable, cost-efficient, and scalable.
If you want to stop experimenting and finally build AI applications that work in the real world-this is the book that gets you there.
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