{"product_id":"hands-on-ai-engineering-machine-learning-writers-9798252097244","title":"Hands-On AI Engineering: Code First Guide to Building Production Grade LLM Systems with Python Accompanied with GitHub Tutorials Learn about Transform","description":"\u003cp\u003e\u003cb\u003e\u003ci\u003e\"All of AI... has a proof-of-concept-to-production gap.\"\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e- Andrew Ng\u003c\/p\u003e\u003cp\u003eThis gap is why most AI projects never make it past the prototype stage.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eHands-On AI Engineering\u003c\/i\u003e is a practical, code-first guide that teaches you how to move from simple experiments to reliable, production-grade AI systems without relying on expensive cloud credits or black-box APIs.\u003c\/p\u003e\u003cp\u003eThis book focuses on the real decisions you face when building AI applications: evaluation strategy, cost control, reliability, guardrails, and deployment trade-offs.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat You'll Learn\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eTraining and fine-tuning neural networks with PyTorch\u003c\/li\u003e\n\u003cli\u003eParameter-efficient fine-tuning using LoRA and QLoRA on consumer GPUs\u003c\/li\u003e\n\u003cli\u003eBuilding robust RAG pipelines (smart chunking, hybrid retrieval, ranking, and faithfulness checks)\u003c\/li\u003e\n\u003cli\u003eProper evaluation methods (rubrics, LLM-as-a-judge, golden datasets, regression testing)\u003c\/li\u003e\n\u003cli\u003eProduction realities: monitoring, guardrails, cost optimization, and reliable deployment\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eTable of contents\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eChapter I - Python Foundations for AI Engineering\u003c\/li\u003e\n\u003cli\u003eChapter II - Deep Learning Fundamentals with PyTorch and TensorFlow\u003c\/li\u003e\n\u003cli\u003eChapter III - Understanding the Transformer Architecture\u003c\/li\u003e\n\u003cli\u003eChapter IV - Understanding Large Language Models (LLMs)\u003c\/li\u003e\n\u003cli\u003eChapter V - Tokenization, Context Windows, and Text Chunking\u003c\/li\u003e\n\u003cli\u003eChapter VI - Working with Hugging Face Transformers\u003c\/li\u003e\n\u003cli\u003eChapter VII - Building AI Applications with LangChain\u003c\/li\u003e\n\u003cli\u003eChapter VIII - Parameter-Efficient Fine-Tuning (PEFT)\u003c\/li\u003e\n\u003cli\u003eChapter IX - Retrieval-Augmented Generation (RAG)\u003c\/li\u003e\n\u003cli\u003eChapter X - Evaluation, Deployment, and Monitoring in AI Systems\u003c\/li\u003e\n\u003cli\u003eChapter XI - Building Your AI Engineering Portfolio\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cp\u003eHands-On AI Engineering gives you the guidance needed to move you from an experiment to a dependable system.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAlso includes 6 fully working GitHub projects\u003c\/b\u003e you can run locally, from basic RAG to evaluated systems, agents with memory, and study tools. These projects mirror modern team workflows and give you something concrete to show in interviews or client work.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Machine Learning Writers\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798252097244\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 03\/18\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 160\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.49lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.34d","brand":"Machine Learning Writers","offers":[{"title":"Paperback","offer_id":48588502761727,"sku":"9798252097244","price":30.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/hands-on-ai-engineering-machine-learning-writers-9798252097244","provider":"WR Book House","version":"1.0","type":"link"}