Hands-On AI Engineering: Code First Guide to Building Production Grade LLM Systems with Python | Accompanied with GitHub - Marjane Mall - Image 1
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Hands-On AI Engineering: Code First Guide to Building Production Grade LLM Systems with Python | Accompanied with GitHub

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Hands-On AI Engineering is a beginner, code-first guide to building production-grade LLM systemsWritten 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 p...

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Description produit

Marque
GENERIC
Titre
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
Éditeur
Independently published
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
3/18/2026 12:00:00 AM
Langue d'origine
English
ISBN
3238288520
Dimensions
6 x 0.36 x 9 inches
Nombre de pages
159 pages
Langue
English
Résumé
Hands-On AI Engineering is a beginner, code-first guide to building production-grade LLM systemsWritten 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 perform under pressure without relying on expensive cloud credits or black-box APIs.What’s included:Training and fine-tuning neural networks with PyTorchParameter-efficient fine-tuning using LoRA and QLoRA on consumer GPUsBuilding robust RAG pipelines (smart chunking, hybrid retrieval, ranking, and faithfulness checks)Proper evaluation methods (rubrics, LLM-as-a-judge, golden datasets, regression testing)Production realities: monitoring, guardrails, cost optimization, and reliable deploymentPerformance 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. Read more
Auteur
Machine Learning Writers
Date de parution
3/18/2026 12:00:00 AM