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New MEAP! Architecting for Autonomy
Businesses of all sizes are actively building intelligent systems designed to reason over enterprise context, coordinate work, make recommendations, act, and adapt over time. In this important book, enterprise AI experts Anjali Jain and Philip O'Shaughnessy introduce the new architectural principles required for successful autonomous agents in production systems. By the end, you’ll have a working Agentic Enterprise Framework, a domain-agnostic toolkit of patterns, playbooks, and reference models that you can apply to make almost any architecture ready for autonomous agents.
As you go, you’ll see relevant examples that reflect the messy reality of enterprise change. You’ll also see the real-world consequences of getting the architecture right, and the cost of getting it wrong. Best of all, you’ll learn to recognize anti-patterns like invisible autonomy, escalation deadlock, and agentic silos before they surface and start causing real problems. By the end of the book, you’ll be able to design enterprise architectures for autonomous systems, turning agentic AI from an isolated pilot into full production-grade capabilities! [Read more]
4 chapters of this new MEAP are available now with more to follow soon!
A hands-on playbook for turning a general-purpose open-weights model into a focused, cost-efficient system that’s tailored to your business. You’ll explore the complete adaptation spectrum, from prompting and RAG, through LoRA and QLoRA, to fully supervised fine-tuning, knowledge distillation, and preference alignment with DPO. [Read more]
5 chapters of this MEAP are available now, with more to follow soon!
Post-training expert Nathan Lambert gives you an insider’s guide to using RLHF to develop human-friendly AI. Through hands-on experiments and mini-implementations, Nathan clearly and concisely introduces post-training techniques that transform a generic base model into AI assistants that are safer, smarter, and aligned with human values. [Read more]
OpenAI co-founder Ilya Sutskever is one of the most important figures in modern deep learning. Along with developing the GPT models, he is the originator of the famous “Sutskever’s List”—a reading list of 30 research papers that cover “90% of what matters” in AI. In this book, AI expert Rich Heimann dives into each of these influential and vitally-important papers, revealing the breakthroughs that shaped Ilya’s thinking and the entire AI field. [Read more]
Learn how to make evidence-based investment decisions on AI projects. Author Christophe De Greift guides you through examples in real estate, retail, manufacturing, finance, and more. You'll learn to apply one-page canvases, problem-solving frameworks, readiness and maturity scorecards, stakeholder interview guides, prioritization models, editable AI prompts, and other practical tools, all with explicit criteria built in. [Read more]
7 chapters of this MEAP are available now, with more to follow soon!
Build an intuitive understanding of machine learning from the ground up. Each chapter introduces a core ML concept, such as regression and tree-based methods, data preprocessing, feature engineering, neural networks, and more. This totally-revised second edition also illuminates modern AI, including transformers, LLMs, and image generation models. [Read more]
10 chapters of this MEAP are available now, with more to follow soon!
An internal developer platform can be a productivity multiplier and give developers flexibility in their infrastructure choices — but first, you need to make the right choices when it comes to building blocks.
The Manning ebook Platform Engineering on Kubernetes takes an in-depth look at the latest best practices and open source tools from the Kubernetes community, including multi-cloud infrastructure management with Crossplane, using Dapr for standard API distribution, implementing feature flags with OpenFeature, and more!
Bestseller! Master and Build Large Language Models—just $10!
In this engaging liveVideo, veteran AI researcher Sebastian Raschka leads you step by step through the inner workings of a large language model. You'll see and hear Sebastian talk you through each step of the LLM project you build in his bestselling book Build a Large Language Model (From Scratch).
This unique course also includes six essential prerequisite videos created by AI expert Abhinav Kimothi, author of A Simple Guide to Retrieval Augmented Generation. Abhinav delivers an insightful review of everything from the Python
features you need to work on LLMs to advanced PyTorch operations, ensuring you can succeed regardless of your starting point. [Read more]
Build a production-style agentic RAG system that turns massive documents like the EU AI Act into trustworthy, citation-backed answers using hybrid retrieval, Pydantic AI, LangGraph, Qdrant, and FastAPI—ready to adapt to any enterprise knowledge base. [Read more]
Explore practical AI problem-solving through three real-world roles: librarian, investigative journalist, and data analyst. Create engaging content around a classic short story, analyze a self-driving car accident report, and use ChatGPT’s Python tools to investigate FAA bird strike data. Along the way, you’ll learn AI productivity techniques, multimodal workflows, and how to use ChatGPT to improve analysis and decision-making. [Read more]
Build an AI-powered chatbot for a local pet refuge that combines expertise with a compassionate personality. Starting with prompt engineering and persona design, you’ll add search, multi-use-case handling, and scheduling features using OpenAI’s API, LangChain, and LangGraph. By the end, you’ll have a capable AI agent that blends empathy, knowledge retrieval, and intelligent routing. [Read more]
Take on the role of a data scientist and build a self-hosted LLM from the ground up. Implement the Llama 3.2 architecture, then fine-tune it for lead classification and HTML code generation. Using Unsloth and free Colab GPUs, you’ll create a flexible, efficient model while gaining hands-on experience with transformers and LLM customization. [Read more]
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