LLM 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

Steven J. Maranto

ISBN 13: 9798185300183

Editore: Independently Published Jul 2026, 2026

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Dati bibliografici

• Titolo: LLM 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
• Editore: Independently Published Jul 2026
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798185300183
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 254 L x 178 A x 12 P (millimetri) 254 millimetri di larghezza per 178 millimetri di altezza per 12 millimetri di profondità
• Peso: 398 grammi

Descrizione articolo

Riassunto

LLM 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

Move beyond LLM demos and learn how production AI systems are actually engineered.

Many 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?

Solution

LLM Systems Engineering for AI Engineers 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.

You will learn how to:

• Set up a clean LLM engineering workspace

• Understand tokens, transformers, sampling, and inference behavior

• Prepare datasets for training, fine-tuning, RAG, and evaluation

• Train a small language model from scratch with PyTorch

• Use Hugging Face, PEFT, LoRA, and QLoRA for fine-tuning

• Build retrieval-augmented generation systems for private knowledge

• Evaluate accuracy, groundedness, safety, latency, and cost

• Deploy, monitor, secure, and maintain production-ready LLM systems

Proof

This 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.