AI Engineering bundle 
Designing AI Systems 
AI applications need much more than a connection to a model. To work well in the real world, they need memory, access to company knowledge, safe ways to take action, clear rules, and reliable operations behind the scenes. Rather than rebuilding those pieces for every project, organizations can create a shared AI platform that supports all their AI applications.Designing AI Systems shows you how to build that platform. Starting from scratch, you’ll create the core services that real AI applications depend on: a way to connect to different AI providers, a way to remember earlier interactions, a way to bring in your organization’s own data, and a way to let AI systems use tools and act safely within company rules. By the end, you’ll understand not just how to call an AI model but how to build the full system around it.
This book focuses on the part of AI development that usually gets overlooked: everything required to make an AI system reliable, manageable, and ready for production. With complete Python code in every chapter, you’ll build a working reference implementation you can adapt to your own needs. You’ll also learn the practical operational skills that matter in production, including managing costs, routing requests, enforcing safety controls, and evaluating system quality.
The result is a solid foundation for AI applications that can use company knowledge, carry out multi-step work, connect to outside tools, and remain trustworthy and governable as they scale.
Generative AI in Action
Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you’ll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You’ll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems.
Machine Learning Engineering in Action
Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You’ll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author’s extensive experience, every method in this book has been used to solve real-world projects.
LLMs in Production
LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi.
Some bundled books and liveVideos are part of the Manning Early Access Program. You'll get all the available content now, new content as it's created, and the final product when it's ready.
