{"product_id":"llm-systems-engineering-for-ai-steven-j-maranto-9798185300183","title":"LLM Systems Engineering for AI Engineers: Build, Fine-Tune, Evaluate, Deploy, and Monitor Production-Ready Large Language Models with Python, PyTorch,","description":"\u003cb\u003eLLM Systems Engineering for AI Engineers: Build, Fine-Tune, Evaluate, Deploy, and Monitor Production-Ready Large Language Models with Python, PyTorch, Hugging Face, RAG, and MLOps\u003c\/b\u003e\u003cp\u003e\u003cb\u003eMove beyond LLM demos and learn how production AI systems are actually engineered.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eMany AI projects start with a promising prompt, then struggle when real users, private data, latency limits, evaluation failures, security risks, and deployment problems appear. How do you choose between RAG, fine-tuning, continued pretraining, hosted APIs, and open models? How do you test whether an LLM system is accurate, safe, cost-aware, and ready for production?\u003c\/p\u003eSolution\u003cp\u003e\u003cb\u003eLLM Systems Engineering for AI Engineers\u003c\/b\u003e gives you a practical engineering path for building large language model applications from prototype to production using Python, PyTorch, Hugging Face, RAG, FastAPI, vector databases, evaluation workflows, deployment practices, monitoring, and MLOps.\u003c\/p\u003e\u003cp\u003eYou will learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003eSet up a clean LLM engineering workspace\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUnderstand tokens, transformers, sampling, and inference behavior\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003ePrepare datasets for training, fine-tuning, RAG, and evaluation\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eTrain a small language model from scratch with PyTorch\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUse Hugging Face, PEFT, LoRA, and QLoRA for fine-tuning\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eBuild retrieval-augmented generation systems for private knowledge\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eEvaluate accuracy, groundedness, safety, latency, and cost\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eDeploy, monitor, secure, and maintain production-ready LLM systems\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003eProof\u003cp\u003eThis book is built for AI engineers, ML engineers, software developers, data scientists, backend engineers, technical founders, and advanced students who want more than prompt experiments. With step-by-step workflows, runnable code examples, project structure, checklists, and a capstone production LLM system, it gives you the practical confidence to design, ship, and operate real AI systems.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Steven J. Maranto\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798185300183\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 07\/02\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 224\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.87lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.47d","brand":"Steven J. Maranto","offers":[{"title":"Paperback","offer_id":49174184591615,"sku":"9798185300183","price":22.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/llm-systems-engineering-for-ai-steven-j-maranto-9798185300183","provider":"WR Book House","version":"1.0","type":"link"}