Engineering AI at Scale: Platforms, RAG, Agents, and Reliability
A learning path curated by Devansh and Manning Publications
This complimentary eBook provides a journey through modern AI engineering. Each chapter is selected from a Manning title, covering the foundations of ML platforms, prompt engineering, enterprise RAG, AI agents, LLM operations, and reliable deployment strategies. Together, they form a cohesive learning path for practitioners building trustworthy, production-ready AI systems.
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What is inside
Our FREE eBook, Engineering AI at Scale: Platforms, RAG, Agents, and Reliability, contains chosen chapters from these bestsellers.
Build a Machine Learning Platform (From Scratch)
Get your machine learning models out of the lab and into production!
Delivering a successful machine learning project is hard. Build a Machine Learning Platform (From Scratch) makes it easier. In it, you’ll design a reliable ML system from the ground up, incorporating MLOps and DevOps along with a stack of proven infrastructure tools including Kubeflow, MLFlow, BentoML, Evidently, and Feast.
Enterprise RAG
Securely blend advanced LLM with your own databases, documentation, and code repos using these techniques for enterprise-quality retrieval augmented generation.
Retrieval Augmented Generation, or RAG, is the gold standard for using domain-specific data, such as internal documentation or company databases, with large language models (LLMs). Creating trustworthy, stable RAG solutions you can deploy, scale, and maintain at the enterprise level means establishing data workflows that maximize accuracy and efficiency, addressing cost and performance problems, and building in appropriate checks for privacy and security. This book shows you how.
LLMs in Production
Learn how to put Large Language Model-based applications into production safely and efficiently.
This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice.
AI Engineering in Practice
Write, refine, organize, and optimize AI prompts that generate relevant and useful text and images!
Generative AI models such as ChatGPT, Stable Diffusion, and Gemini can produce amazingly “human-like” news articles, document summaries, images, computer code, and more—if you know how to write effective prompts. This book will teach you the prompt design and authoring skills you need to get useful and relevant responses from AI models, along with advanced prompting techniques for Retrieval Augmented Generation (RAG), building autonomous agents, and data privacy.
AI Agents in Action
Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs.
From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks.
Building Reliable AI Systems
Tested strategies to reduce hallucinations, improve performance and cost efficiency, and reduce bias or unethical behavior in your LLMs outputs.
Building Reliable AI Systems shows you exactly how to guide large language models from research prototypes to scalable, robust, and efficient production systems. From model training to maintenance, an engineer will find everything they need to work with LLMs in this one-stop guide.
