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2 548
Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications 2026
Автор: Valliappa Lakshmanan
Generative AI enables powerful new capabilities, but they come with some serious limitations that you'll have to tackle to ship a reliable application or agent. Luckily, experts in the field have compiled a library of 32 tried-and-true design patterns to address the challenges you're likely to encounter when building applications using LLMs, such as hallucinations, nondeterministic responses, and knowledge cutoffs.
This book codifies research and real-world experience into advice you can incorporate into your projects. Each pattern describes a problem, shows a proven way to solve it with a fully coded example, and discusses trade-offs.2 548
Azure AI Fundamentals (AI-900) Study Guide: In-Depth Exam Prep and Practice 2025
Автор: Tom Taulli
Businesses that want to stay competitive know that AI has become a crucial technology—and so do their employees looking to grow their careers. Earning Microsoft's AI-900: Azure AI Fundamentals certification proves your proficiency with foundational AI concepts. This study guide equips you with the knowledge needed to pass the AI-900 exam, whether you're an IT professional, a data analyst, or a student looking to break into the AI field.
Packed with clear explanations, real-world examples, exam tips, and practice questions, this comprehensive handbook is your go-to resource for mastering the Azure AI platform and advancing your career. You'll explore key exam topics, including machine learning, computer vision, and generative AI, while gaining practical insights into leveraging Azure's powerful AI tools.2 548
Cilium: Up and Running: Cloud Native Networking, Security, and Observability 2026
Автор: Nico Vibert
Cilium is now considered the de facto cloud native networking platform for Kubernetes, connecting, securing, and monitoring millions of applications across thousands of clusters. With such versatility and feature-richness, Cilium can be daunting to learn. This comprehensive guide breaks Cilium down, making it broadly accessible to the increasing number of users who'll encounter the platform in their careers.
Nico Vibert, Filip Nikolic, and James Laverack, all from Isovalent (creators of eBPF and Cilium), take you through how Cilium works, the problems it can solve, and how to run it in production. If you're an experienced platform engineer or network architect who wants to get on top of the next big thing in cloud networking, this book is for you.2 548
Название: Large Language Models: The Hard Parts: Open Source AI Solutions for Common Pitfalls (Final Release) 2026
Автор: Thársis T.P.
Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. Large Language Models: The Hard Parts offers a clear, practical examination of the limitations developers and AI engineers face when building LLM-based applications. With a focus on implementation pitfalls (not just capabilities), this book provides actionable strategies supported by reproducible Python code and open source tools.
Readers will learn how to navigate key obstacles in application evaluation, input management, testing, and safety. Designed for builders and technical product leads, this guide emphasizes practical solutions to real-world problems and promotes a grounded understanding of LLM constraints and trade-offs.2 548
Introduction to Data Science for Engineering Students 2026
Автор: Ilias Bilionis
This book offers engineering students a concise and practical introduction to Data Science — no prior experience required. Designed specifically for those new to programming and statistical analysis, the book introduces the essential tools and concepts behind today's predictive AI systems.
Based on a proven course at Purdue University, Introduction to Data Science for Engineering Students equips students with core Data Science knowledge, such as Python programming, data analysis techniques, and key foundational statistical concepts necessary for predictive modelling. Through real-world engineering examples (e.g. predicting engine efficiency), students learn how to visualize and analyze real experimental data, apply probability to manage uncertainty, and learn how to build reliable predictive models step-by-step.2 548
Hands-On AWS CDK: Building Cloud Native Applications with Infrastructure as Code 2025
Автор: Sam Ward Biddle
Looking to accelerate development and build cloud native applications with AWS Cloud Development Kit? Through hands-on projects, you'll learn the basics of AWS CDK, the tool of choice for many of the world's largest technology companies.
Informed by real case studies and years of work with enterprise-scale cloud architectures, this book will benefit both novice and advanced cloud developers. It's complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions to help you build a shareable portfolio of completed projects, demonstrating your ability to build cloud infrastructure at scale.2 548
Domain-Specific Small Language Models: Efficient AI for local deployment (Final Release) 2026
Автор: Guglielmo Iozzia
Domain-Specific Small Language Models teaches you how to create language models that deliver the power of LLMs for specific areas of knowledge. It provides a practical, application-focused counterpart to foundational texts like Sebastian Raschka’sBuild a Large Language Model (From Scratch), showing you how to adapt large-scale concepts for efficient, specialized use. You’ll learn to minimize the computational horsepower your models require, while keeping high–quality performance times and output. You’ll appreciate the clear explanations of complex technical concepts alongside working code samples you can run and replicate on your laptop. Plus, you’ll learn to develop and deliver RAG systems and AI agents that rely solely on SLMs, and without the costs of foundation model access.2 548
Generative AI on Kubernetes: Operationalizing Large Language Models (Final Release) 2026
Автор: Roland Huß
Generative AI is revolutionizing industries, and Kubernetes has fast become the backbone for deploying and managing these resource-intensive workloads. This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to combine AI innovation with the power of cloud native infrastructure. Authors Roland Huß and Daniele Zonca provide a clear road map for training, fine-tuning, deploying, and scaling GenAI models on Kubernetes, addressing challenges like resource optimization, automation, and security along the way.2 548
AI-Ready Data Blueprints: From Raw Data to AI-Driven Innovation (Final Release) 2026
Автор: Navnit Shukla
Companies innovating with Generative AI understand that having the right data foundation is critical for success and profitability. To best position themselves for long-term success, organizations must prioritize investments in data and AI governance. AI-Ready Data Blueprints is your map to connecting data strategy, GenAI, and ethical practices to build and scale truly effective solutions.
Taking a comprehensive, cloud-agnostic approach focused on real-world business challenges, seasoned data and AI experts Navnit Shukla, Kien Pham, Srikanth Sopirala, and Harsha Tadiparthi share actionable insights to guide you in designing and implementing effective data-centric GenAI systems. Whether you're new to GenAI or are already focusing on optimizing it for accuracy, speed, or both, the principles shared in this book will empower you to excel in all your AI endeavors.2 548
C++ For Dummies, 8th Edition 2026
Автор: Bradley L. Jones
An accessible walkthrough of one of the world's most popular programming languages: C++.
C++ For Dummies is your from-scratch guide that explains the essentials of what you need to know to understand the language and build your very first program in C++. Bradley Jones, experienced programmer and coding educator, packs this edition with examples and clear demonstrations that explain the “why” and the “how” of programming in C++, as well as the programming concepts that will form the foundation of your code, including classes, loops, classes, objects, inheritance, and more.