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

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

$35.99 $43.99
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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
  • 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
  • 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
  • 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
  • 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
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

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

Machine Learning Writers
Paperback
Computers Technology, Business Technology
$35.99 $43.99

Hands-On AI Engineering covers both the construction of LLM systems and the core challenges modern AI teams face daily: performance limits, reliability, evaluation, and cost control.

Written by 4 practicing AI engineers. Inside you’ll learn how to design, build, and operate LLM systems that run efficiently, scale, and perform under pressure. Using local, open-source tools, without expensive cloud credits or black-box APIs.

What’s included…

  • Training and Fine-Tuning Neural Networks with PyTorch. A set framework on how to effectively train and adapt models using PyTorch.
  • Parameter-efficient fine-tuning with LoRA and QLoRA, and how these techniques make it practical to customize large models on consumer GPUs.
  • Building robust RAG pipelines through smart chunking, hybrid retrieval, intelligent ranking, and faithfulness/grounding mechanisms.
  • Proper evaluation methods including rubrics, LLM-as-a-judge, golden datasets, and regression testing for reliable assessment.
  • Production realities. Key insights into monitoring, guardrails, cost optimization, and reliable deployment of LLM systems.


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.



These projects mirror modern team workflows and give you something concrete to show in interviews or client work.

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