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 - Tapa blanda

Sinopsis

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.

• 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

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.

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