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

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.