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Hands-On AI Engineering: Build Applications with Python, Transformers, Prompt, Foundation Models, LLMs, ML Pipelines, and System Building

by Machine Learning Writers
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Book cover type: Paperback
  • ISBN13: 9798279292356
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 162
  • Original Price: USD 45.95
  • Language: English
  • Edition: N/A
  • Item Weight: 309 grams
  • BISAC Subject(s): Logic Design

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

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