The books behind the guides.

Roberto Infante

A hands-on developer guide that takes you from LLM prompt basics through advanced RAG, multi-tool agents, multi-agent systems, and the Model Context Protocol using LangChain, LangGraph, and LangSmith.

Chip Huyen

A comprehensive engineering guide for building production-ready AI applications on top of foundation models, covering the full stack from evaluation and prompt engineering to RAG, finetuning, inference optimization, and deployment architecture.

Manning (MEAP)

A hands-on guide to building LLM agents and multi-agent systems from scratch by developing your own educational framework with support for tools, MCP, Skills, memory, human-in-the-loop, and the A2A protocol.

Manning (MEAP)

A hands-on guide to building autonomous AI agents from scratch by focusing on the core principle of Context Engineering, empowering developers to understand, debug, and create their own agent frameworks.

Manning (MEAP)

This book provides a comprehensive engineering framework for building, deploying, and maintaining reliable, trustworthy, and production-ready AI systems powered by Large Language Models.

Immanuel Trummer

A hands-on guide showing developers and data scientists how to use large language models—across text, tables, images, audio, and graphs—to build effective, cost-efficient data analysis pipelines in Python.

A comprehensive textbook that introduces the mathematical foundations, modern practical techniques, and advanced research topics of deep learning for students and software engineers.

Chip Huyen

A holistic, iterative framework for designing production-ready machine learning systems that are reliable, scalable, maintainable, and adaptive across every stage from data engineering to continual learning.

Manning (MEAP)

A practical guide for engineers to build, optimize, and deploy cost-effective and secure small language models (SLMs) on commodity hardware for specialized, real-world applications.

David Foster

A hands-on technical guide to building generative deep learning models that can paint, write, compose music, and play games by teaching machines to create original content through VAEs, GANs, RNNs, and reinforcement learning.

Valliappa Lakshmanan, Sara Robinson

A catalog of thirty reusable design patterns that provide proven solutions to common challenges in data preparation, model building, and MLOps for machine learning practitioners.

Andriy Burkov

A practical, end-to-end guide to the engineering principles and best practices required to successfully build, deploy, and maintain machine learning systems in production.

Lewis Tunstall, Leandro von Werra

A hands-on guide for data scientists and machine learning engineers to build, train, and optimize state-of-the-art language applications using transformer models with the Hugging Face ecosystem.

Oliver Dürr, Beate Sick & Elvis Murina

A hands-on guide to building probabilistic deep learning models using the maximum likelihood principle and Bayesian inference, implemented in Python with Keras and TensorFlow Probability.

Cathy Chen, Niall Richard Murphy

A practical guide to operating machine learning systems reliably in production by applying Site Reliability Engineering (SRE) principles across the entire ML lifecycle.

Craig Walls

A hands-on guide for Java developers to build production-grade generative AI applications using Spring AI, covering everything from basic prompting to RAG, tools, MCP, multimodal generation, observability, security, and autonomous agents.

Andriy Burkov

A compact, practical field guide to the core ideas, algorithms, and workflow of modern machine learning, distilling decades of research into what actually matters in practice.

Marco Peixeiro

A hands-on practitioner's guide to understanding, applying, fine-tuning, and comparing foundation models—from TimeGPT to LLM-based approaches—for time-series forecasting and anomaly detection.

Simon J. D. Prince

A comprehensive conceptual guide to deep learning that builds from fundamental supervised learning through generative models and reinforcement learning, candidly acknowledging what remains unknown about why deep learning works.