Hands-On AI Engineering: Code First Guide to Building Production Grade LLM Systems with Python | Accompanied with GitHub Tutorials | Learn about Transformers Foundation Models & ML Pipelines - Couverture souple

Writers, Machine Learning

 
9798252097244: Hands-On AI Engineering: Code First Guide to Building Production Grade LLM Systems with Python | Accompanied with GitHub Tutorials | Learn about Transformers Foundation Models & ML Pipelines
Couverture souple
ISBN 13 : 9798252097244
Editeur : Independently published, 2026

Synopsis

Hands-On AI Engineering is a practical, code-first guide to building production-grade LLM systems.

Written by 4 practicing AI engineers. It focuses on what AI teams deal with every day: performance limits, reliability, evaluation, and cost control.

You’ll learn how to design, build, and operate LLM systems that run efficiently, scale responsibly, and hold up under real users — without relying on expensive cloud credits or black-box APIs.

What this book covers
  • Training and fine-tuning neural networks with PyTorch
  • Fine-tuning transformers using LoRA and QLoRA on consumer hardware
  • Building robust RAG pipelines: chunking strategies, hybrid retrieval, ranking, and faithfulness checks
  • Deploying models with FastAPI
  • Evaluating systems properly: rubrics, LLM-as-a-judge, golden datasets, regression testing, benchmarking
  • Monitoring, failure handling, and cost–performance trade-offs
  • Documenting architectures and decisions so teams can trust and extend your work


Performance add-ons (last chapter)

A companion GitHub repository, carefully sequenced projects you can follow along with and build yourself.

  • Project 1 - Simple Companion Chat: Basic chatbot built around a single document.
  • Project 2 - Personal Knowledge Q&A: Ask questions over your own files with grounded answers.
  • Project 3 - Checked Q&A System: Compare AI answers against expected results.
  • Project 4 - Conversational Agent: Multi-turn chat with memory and simple tools.
  • Project 5 - Document Summarizer: Controlled summaries with basic quality checks.
  • Project 6 - Chapter Explorer: Turn text into outlines and short quizzes.



This book gives you the engineering mindset needed to move from experiments to dependable systems.

The projects are designed to reflect real-world workflows which you can discuss confidently in interviews and use to stand out as an AI engineer.

Use wisely.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Éditeur
Independently published
Date d'édition
2026
Langue
anglais
ISBN 13 
9798252097244
Reliure
Broché
Nombre de pages
159
Coordonnées du fabricant
Manufactured by Amazon on behalf of the author
https://www.amazon.fr/hz/contact-us

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