Artificial intelligence is entering a new phase. No longer limited to answering prompts or completing simple writing tasks, AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting creative work, these systems are quickly reshaping industries and everyday life. This book provides the conceptual foundation and practical insights you need to understand—and effectively work with—this emerging technology. Through hundreds of clear graphic illustrations, Maarten Grootendorst and Jay Alammar explain how AI agents are built, how they think, and where they're heading. Designed for professionals, students, and curious learners alike, this guide goes beyond the buzz to reveal what's actually happening inside these systems, why it matters, and how to apply the knowledge in real-world contexts. With its visual storytelling and accessible explanations, An Illustrated Guide to AI Agents is your essential reference for navigating the next frontier of artificial intelligence. Explore the core architecture of AI agents: tools, memory, and planning Understand reasoning LLMs, multimodal models, and multi-agent collaboration Learn advanced methods, including distillation, quantization, and reinforcement learning Evaluate real-world applications, strengths, and limitations of AI agents

Since its release in late 2024, Anthropic's Model Context Protocol (MCP) has redefined how developers build and connect AI agents to tools, data, and each other. AI Agents with MCP is the first comprehensive guide to this rapidly emerging standard, helping engineers unlock its full potential with hands-on projects. Whether you're developing agentic workflows, bridging tools across platforms, or creating robust multiagent systems, this book walks you through every layer of MCP--from protocol structure to server and client implementation. Author Kyle Stratis provides the practical expertise needed to build fully functional MCP servers, clients, and more. Unlike high-level overviews or fragmented documentation, this book gives you a deep systems-level understanding of MCP's capabilities--and limitations. With its flexible, model-agnostic design, MCP continues to gain traction across the generative AI community; this book ensures you're ready to build with it confidently and effectively. Understand the structure and core concepts of the Model Context Protocol Build complete MCP servers, clients, and transport layers in Python Consume tools, prompts, and data via MCP-based agent workflows Extend agent capabilities with MCP for large-scale and AI-native systems

Companies are now moving generative AI projects from the lab to production environments. To support these increasingly sophisticated applications, they're turning to advanced practices such as multiagent architectures and complex code-based frameworks. This practical handbook shows you how to leverage cutting-edge techniques using Microsoft's powerful ecosystem of tools to deploy trustworthy AI systems tailored to your organization's needs. Written for and by AI professionals, Generative AI on Microsoft Azure goes beyond the technical core aspects, examining underlying principles, tools, and practices in depth, from the art of prompt engineering to strategies for fine-tuning models to advanced techniques like retrieval-augmented generation (RAG) and agentic AI. Through real-world case studies and insights from top experts, you'll learn how to harness AI's full potential on Azure, paving the way for groundbreaking solutions and sustainable success in today's AI-driven landscape. Understand the technical foundations of generative AI and how the technology has evolved over the last few years Implement advanced GenAI applications using Microsoft services like Azure AI Foundry, Copilot, GitHub Models, Azure Databricks, and Snowflake on Azure Leverage patterns, tools, frameworks, and platforms to customize AI projects Manage, govern, and secure your AI-enabled systems with responsible AI practices Build upon expert guidance to avoid common pitfalls, future-proof your applications, and more

As artificial intelligence becomes central to modern product design, UX professionals must adapt their toolkits to meet new demands. In Designing AI Interfaces, senior product designer Louise Macfadyen offers a timely, practice-oriented guide for building intuitive, ethical, and effective user experiences with large language models (LLMs) and autonomous AI systems. From content moderation to interruptibility, this book presents actionable design patterns for today's most advanced AI interactions—with clear technical insights to help designers understand how AI systems process inputs, generate outputs, and make decisions on users' behalf. Written specifically for product designers navigating the AI transition, this book provides concrete strategies for managing risk, enabling transparency, and fostering user trust in increasingly agentic systems. Readers will learn how to enable users to steer and shape AI responses in real time, incorporate ethical and UX principles into actionable design strategies, and navigate trade-offs in autonomy and control—all while gaining fluency in key AI concepts to collaborate more effectively with engineering teams. Design effective and ethical interfaces for LLMs and AI agents Apply best-practice patterns for content warnings, permissions, and oversight Gain a mental model for how AI systems reason and act Collaborate confidently with engineering and product teams Evaluate your org's AI maturity and advocate for responsible implementation

Learning AutoML is your practical guide to applying automated machine learning in real-world environments. Whether you're a data scientist, ML engineer, or AI researcher, this book helps you move beyond experimentation to build and deploy high-performing models with less manual tuning and more automation. Using AutoGluon as a primary toolkit, you'll learn how to build, evaluate, and deploy AutoML models that reduce complexity and accelerate innovation. Author Kerem Tomak shares insights on how to integrate models into end-to-end deployment workflows using popular tools like Kubeflow, MLflow, and Airflow, while exploring cross-platform approaches with Vertex AI, SageMaker Autopilot, Azure AutoML, Auto-sklearn, and H2O.ai. Real-world case studies highlight applications across finance, healthcare, and retail, while chapters on ethics, governance, and agentic AI help future-proof your knowledge. Build AutoML pipelines for tabular, text, image, and time series data Deploy models with fast, scalable workflows using MLOps best practices Compare and navigate today's leading AutoML platforms Interpret model results and make informed decisions with explainability tools Explore how AutoML leads into next-gen agentic AI systems

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 unlock 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. With actionable insights with real-world examples, readers will learn to tackle the opportunities and complexities of managing GenAI applications in production environments. Whether you're experimenting with large-scale language models or facing the nuances of AI deployment at scale, you'll uncover expertise you need to operationalize this exciting technology effectively. Learn to run GenAI models on Kubernetes for efficient scalability Get techniques to train and fine-tune LLMs within Kubernetes environments See how to deploy production-ready AI systems with automation and resource optimization Discover how to monitor and scale GenAI applications to handle real-world demand Uncover the best tools to operationalize your GenAI workloads Learn how to run agent-based and AI-driven applications

In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture. Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions. Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using Databricks Leverage LLMs to extract deeper, actionable insights from your business data residing in lakehouses Discover how to integrate traditional ML and GenAI models for customized, scalable solutions Utilize open source models to control costs while maintaining model performance and efficiency Implement best practices for optimizing ML and GenAI models within the Databricks platform

AI Data Center Network Design and Technologies Designing the Networks that Power the AI Revolution Artificial intelligence is transforming the modern data center. Training large-scale machine learning models requires infrastructure that can move massive datasets at lightning speed-far beyond the capabilities of traditional architectures. AI Data Center Network Design and Technologies is the first comprehensive, vendor-neutral guide to building and optimizing networks purpose-built for AI workloads. Written by leading experts in AI data center design, this book bridges the gap between network engineering and AI infrastructure-helping you understand how to design, scale, and future-proof high-performance environments for training and inference. What You'll Learn Architect for scale: Build high-radix network fabrics to support GPU, TPU, and xPU-based AI clusters Optimize data movement: Integrate lossless Ethernet/IP fabrics for high-throughput, low-latency communication Design with purpose: Align network design to AI/ML workload patterns and server architectures Plan for the physical layer: Address cooling, power, and interconnect challenges at AI scale Stay ahead of innovation: Explore emerging standards from the Ultra Ethernet Consortium (UEC) Validate performance: Apply proven deployment, testing, and measurement best practices Why Read This Book AI is redefining what data centers can-and must-do. Whether you're a network engineer, architect, or technology leader, this book provides the technical foundation and forward-looking insights you need to design next-generation networks optimized for AI-scale computing. .

"AI Native LLM Security" is your essential guide to understanding and securing large language models and AI systems. With a focus on implementing practical strategies and leveraging frameworks like OWASP Top 10, this book equips professionals to identify and mitigate risks effectively. By reading this, you'll gain the expertise to confidently manage LLM security challenges. What this Book will help me do Learn about adversarial AI attacks and methods to defend against them. Understand secure-by-design methodologies and their application to LLM systems. Gain insights on implementing MLSecOps practices for robust AI security. Navigate ethical considerations and legal aspects of AI security. Secure AI development life cycles with practical strategies and standards. Author(s) The authors, Vaibhav Malik, Ken Huang, and Adam Dawson, are experts in AI security with collective experience covering cybersecurity, AI development, and security frameworks. Their dedication to advancing trustworthy AI ensures that this book is both technically comprehensive and approachable. Who is it for? This book is perfect for cybersecurity experts, AI developers, and technology managers aiming to secure and manage AI systems. Readers should have a basic understanding of AI and security concepts. If you're a security architect, ML engineer, DevOps professional, or a leader overseeing AI initiatives, this book will help you address LLM security effectively for your field.

Transform Your Business with Intelligent AI to Drive Outcomes Building reactive AI applications and chatbots is no longer enough. The competitive advantage belongs to those who can build AI that can respond, reason, plan, and execute. Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment takes you beyond basic chatbots to create fully functional, autonomous agents that automate real workflows, enhance human decision-making, and drive measurable business outcomes across high-impact domains like customer support, finance, and research. Whether you're a developer deploying your first model, a data scientist exploring multi-agent systems and distilled LLMs, or a product manager integrating AI workflows and embedding models, this practical handbook provides tried and tested blueprints for building production-ready systems. Harness the power of reasoning models for applications like computer use, multimodal systems to work with all kinds of data, and fine-tuning techniques to get the most out of AI. Learn to test, monitor, and optimize agentic systems to keep them reliable and cost-effective at enterprise scale. Master the complete agentic AI pipeline Design adaptive AI agents with memory, tool use, and collaborative reasoning capabilities Build robust RAG workflows using embeddings, vector databases, and LangGraph state management Implement comprehensive evaluation frameworks beyond accuracy, including precision, recall, and latency metrics Deploy multimodal AI systems that seamlessly integrate text, vision, audio, and code generation Optimize models for production through fine-tuning, quantization, and speculative decoding techniques Navigate the bleeding edge of reasoning LLMs and computer-use capabilities Balance cost, speed, accuracy, and privacy in real-world deployment scenarios Create hybrid architectures that combine multiple agents for complex enterprise applications Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Secure your expertise in the next wave of computing with this essential book, which provides a comprehensive guide to Edge AI, detailing its foundational concepts, deployment strategies, and real-world applications for revolutionizing performance and privacy across various industries. Edge AI has the potential to bring the computational power of AI algorithms closer to where data is generated, processed, and utilized. Traditionally, AI models are deployed in centralized cloud environments, leading to latency issues, bandwidth constraints, and privacy concerns. Edge AI addresses these limitations by enabling AI inference and decision-making directly on edge devices, such as smartphones, IoT sensors, and edge servers. Despite its challenges, edge AI presents numerous opportunities across various domains. From real-time health monitoring and predictive maintenance in industrial IoT to personalized recommendations in retail and immersive experiences in augmented reality, edge AI has the potential to revolutionize how we interact with technology. This book aims to provide a comprehensive exploration of edge AI, covering its foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, emerging trends, and real-world applications. This guide is essential for anyone pushing the boundaries to leverage edge computing for enhanced performance and efficiency. Readers will find this volume: Dives deep into the world of edge AI with a comprehensive exploration covering foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, governance frameworks, optimization techniques, and real-world applications; Offers practical guidance on implementing edge AI solutions effectively in various domains, including architecture design, development frameworks, deployment strategies, and optimization techniques; Explores concrete examples of edge AI applications across diverse domains such as healthcare, industrial IoT, smart cities, and autonomous systems, providing insights into how edge AI is revolutionizing industries and everyday life; Provides insights into emerging trends and technologies in the field of edge AI, including convergence with blockchain, augmented reality, virtual reality, autonomous systems, personalized experiences, and cybersecurity. Audience Researchers, AI experts, and industry professionals in the field of computer science, IT, and business management.

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can't do Learn the basics of machine learning code and how it integrates with large datasets Understand the mathematical principles that AI uses to make itself smarter Consider real-world applications of machine learning and write your own algorithms With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

Deliver measurable business value by applying strategic, technical, and ethical frameworks to AI initiatives at scale Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Build AI strategies that align with business goals and maximize ROI Implement enterprise-ready frameworks for MLOps, LLMOps, and Responsible AI Learn from real-world case studies spanning industries and AI maturity levels Book Description AI is only as valuable as the business outcomes it enables, and this hands-on guide shows you how to make that happen. Whether you’re a technology leader launching your first AI use case or scaling production systems, you need a clear path from innovation to impact. That means aligning your AI initiatives with enterprise strategy, operational readiness, and responsible practices, and The AI Optimization Playbook gives you the clarity, structure, and insight you need to succeed. Through actionable guidance and real-world examples, you’ll learn how to build high-impact AI strategies, evaluate projects based on ROI, secure executive sponsorship, and transition prototypes into production-grade systems. You’ll also explore MLOps and LLMOps practices that ensure scalability, reliability, and governance across the AI lifecycle. But deployment is just the beginning. This book goes further to address the crucial need for Responsible AI through frameworks, compliance strategies, and transparency techniques. Written by AI experts and industry leaders, this playbook combines technical fluency with strategic perspective to bridge the business–technology divide so you can confidently lead AI transformation across the enterprise. Email sign-up and proof of purchase required What you will learn Design business-aligned AI strategies Select and prioritize AI projects with the highest potential ROI Develop reliable prototypes and scale them using MLOps pipelines Integrate explainability, fairness, and compliance into AI systems Apply LLMOps practices to deploy and maintain generative AI models Build AI agents that support autonomous decision-making at scale Navigate evolving AI regulations with actionable compliance frameworks Build a future-ready, ethically grounded AI organization Who this book is for This book is for AI/ML leaders and business leaders, CTOs, CIOs, CDAOs, and CAIOs, responsible for driving innovation, operational efficiency, and risk mitigation through artificial intelligence. You should have familiarity with enterprise technology and the fundamentals of AI solution development.

Get up to speed on a new unified approach to building machine learning (ML) systems with a feature store. Using this practical book, data scientists and ML engineers will learn in detail how to develop and operate batch, real-time, and agentic ML systems. Author Jim Dowling introduces fundamental principles and practices for developing, testing, and operating ML and AI systems at scale. You'll see how any AI system can be decomposed into independent feature, training, and inference pipelines connected by a shared data layer. Through example ML systems, you'll tackle the hardest part of ML systems--the data, learning how to transform data into features and embeddings, and how to design a data model for AI. Develop batch ML systems at any scale Develop real-time ML systems by shifting left or shifting right feature computation Develop agentic ML systems that use LLMs, tools, and retrieval-augmented generation Understand and apply MLOps principles when developing and operating ML systems

Master the cutting-edge field of computer vision and artificial intelligence with this accessible guide to the applications of machine learning and deep learning for real-world solutions in robotics, healthcare, and autonomous systems. Applied Computer Vision through Artificial Intelligence provides a thorough and accessible exploration of how machine learning and deep learning are driving breakthroughs in computer vision. This book brings together contributions from leading experts to present state-of-the-art techniques, tools, and frameworks, while demonstrating this technology’s applications in healthcare, autonomous systems, surveillance, robotics, and other real-world domains. By blending theory with hands-on insights, this volume equips readers with the knowledge needed to understand, design, and implement AI-powered vision solutions. Structured to serve both academic and professional audiences, the book not only covers cutting-edge algorithms and methodologies but also addresses pressing challenges, ethical considerations, and future research directions. It serves as a comprehensive reference for researchers, engineers, practitioners, and graduate students, making it an indispensable resource for anyone looking to apply artificial intelligence to solve complex computer vision problems in today’s data-driven world.

Master the next frontier of technology with this book, which provides an in-depth guide to adaptive artificial intelligence and its ability to create flexible, self-governed systems in dynamic industries. Adaptive artificial intelligence represents a significant advancement in the development of AI systems, particularly within various industries that require robust, flexible, and responsive technologies. Unlike traditional AI, which operates based on pre-defined models and static data, adaptive AI is designed to learn and evolve in real time, making it particularly valuable in dynamic and unpredictable environments. This capability is increasingly important in disciplines such as autonomous systems, healthcare, finance, and industrial automation, where the ability to adapt to new information and changing conditions is crucial. In industry development, adaptive AI drives innovation by enabling systems that can continuously improve their performance and decision-making processes without the need for constant human intervention. This leads to more efficient operations, reduced downtime, and enhanced outcomes across sectors. As industries increasingly rely on AI for critical functions, the adaptive capability of these systems becomes a cornerstone for achieving higher levels of automation, reliability, and intelligence in technological solutions. Readers will find the book: Introduces the emerging concept of adaptive artificial intelligence; Explores the many applications of adaptive artificial intelligence across various industries; Provides comprehensive coverage of reinforcement learning for different domains. Audience Research scholars, IT professionals, engineering students, network administrators, artificial intelligence and deep learning experts, and government research agencies looking to innovate with the power of artificial intelligence.

The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place. With an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch—from basic regression techniques to advanced neural networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to build intelligent systems. Understand ML basics, including concepts like overfitting and hyperparameter tuning Complete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluation Learn techniques for unsupervised learning, such as clustering and anomaly detection Build advanced architectures like transformers and diffusion models with PyTorch Harness the power of pretrained models—including LLMs—and learn to fine-tune them Train autonomous agents using reinforcement learning

This book is a comprehensive guide for anyone in the aeronautical and aerospace fields who wants to understand and leverage the transformative power of artificial intelligence to enhance safety, optimize performance, and drive innovation. The field of aeronautical and aerospace engineering is on the brink of a transformative revolution driven by rapid advancements in artificial intelligence (AI). This book analyzes AI’s multifaceted impact on the industry, exploring AI’s potential to address complex challenges, optimize processes, and push technological boundaries with a focus on enhancing safety, security, innovation, and performance. By blending technical insights with practical applications, it provides readers with a roadmap for harnessing AI to solve complex challenges and improve efficiency in aeronautics. Ideal for those seeking a deeper understanding of AI’s role in aeronautical and aerospace engineering, this book offers real-world applications, case studies, and expert insights, making it a valuable resource for anyone aiming to stay at the forefront of this rapidly evolving field. Readers will find this book: Examines AI’s transformative role in aerospace and aeronautics, from enhancing safety to driving innovation and optimizing performance; Highlights real-time applications, addressing AI’s role in boosting operational efficiency and safety in the aerospace and aeronautical industries; Offers insights into emerging AI technologies shaping the future of aerospace and aeronautical systems; Features real-world case studies on AI applications in autonomous navigation, predictive maintenance of aircraft, and air traffic management. Audience Aeronautical and aerospace engineers, AI researchers, students, and industry professionals seeking to understand and apply AI solutions in areas like safety, security, and performance optimization.

AI-Driven Software Testing explores how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing quality engineering (QE), making testing more intelligent, efficient, and adaptive. The book begins by examining the critical role of QE in modern software development and the paradigm shift introduced by AI/ML. It traces the evolution of software testing, from manual approaches to AI-powered automation, highlighting key innovations that enhance accuracy, speed, and scalability. Readers will gain a deep understanding of quality engineering in the age of AI, comparing traditional and AI-driven testing methodologies to uncover their advantages and challenges. Moving into practical applications, the book delves into AI-enhanced test planning, execution, and defect management. It explores AI-driven test case development, intelligent test environments, and real-time monitoring techniques that streamline the testing lifecycle. Additionally, it covers AI’s impact on continuous integration and delivery (CI/CD), predictive analytics for failure prevention, and strategies for scaling AI-driven testing across cloud platforms. Finally, it looks ahead to the future of AI in software testing, discussing emerging trends, ethical considerations, and the evolving role of QE professionals in an AI-first world. With real-world case studies and actionable insights, AI-Driven Software Testing is an essential guide for QE engineers, developers, and tech leaders looking to harness AI for smarter, faster, and more reliable software testing. What you will learn: • What are the key principles of AI/ML-driven quality engineering • What is intelligent test case generation and adaptive test automation • Explore predictive analytics for defect prevention and risk assessment • Understand integration of AI/ML tools in CI/CD pipelines Who this book is for: Quality Engineers looking to enhance software testing with AI-driven techniques. Data Scientists exploring AI applications in software quality assurance and engineering. Software Developers – Engineers seeking to integrate AI/ML into testing and automation workflows.

Dive into the world of architecting intelligent software with this comprehensive guide. This book explores the principles and practices required to integrate artificial intelligence into existing architectures to deliver scalable and robust AI-driven systems. By the end of this journey, you will be equipped with the knowledge and skills to design and optimize next-generation AI applications. What this Book will help me do Effectively integrate AI-driven components within traditional software systems while maintaining scalability and performance. Understand key architectural risks and how to address them, ensuring resilience and cost-efficiency. Apply architectural principles through hands-on exercises and real-world case studies to solidify your learning. Master AI and ML concepts crucial to modern architectures, such as inference and decision-making mechanisms. Develop actionable architectural strategies for implementing user-centric, high-performance AI systems. Author(s) Richard D Avila and Imran Ahmad bring decades of experience in software architecture and AI technologies. Richard has worked extensively in crafting AI-integrated solutions for enterprise-grade systems, while Imran specializes in making complex AI accessible and manageable for developers. Their combined expertise provides an authoritative and approachable guide to AI systems architecture. Who is it for? This book is ideal for software architects and system designers looking to understand and implement AI within their architectures. It is also a valuable resource for CTOs, VPs of Engineering, and professionals spinning on the edge of technical leadership to keep their systems competitive. Intermediate-level developers aspiring to grow into architectural roles will gain actionable insights into the principles of AI-driven systems design. Beginner architects with a passion for AI technologies will find this book to be a robust starting point.

Master Generative AI in software development with hands-on guidance, from coding and debugging to testing and deployment, using GitHub Copilot, Amazon Q Developer, and OpenAI APIs to build scalable, AI-powered applications Key Features Hands-on guidance for mastering AI-powered coding, debugging, and deployment with real-world examples Comprehensive coverage of GenAI concepts, prompt engineering, fine-tuning, and SDLC integration Practical strategies for architecting and scaling production-ready AI-driven applications Book Description Generative AI for Software Developers is your practical guide to mastering AI-powered development and staying ahead in a fast-changing industry. Through a structured, hands-on approach, this book helps you understand, implement, and optimize Generative AI in modern software engineering. From AI-assisted coding, debugging, and documentation to testing, deployment, and system design, it equips you with the skills to integrate AI seamlessly into your workflows. You’ll work with tools such as GitHub Copilot, Amazon Q Developer, and OpenAI APIs while learning strategies for prompt engineering, fine-tuning, and building scalable AI-powered applications. Featuring real-world use cases, best practices, and expert insights, this book bridges the gap between experimenting with AI and production deployment. Whether you’re an aspiring AI developer, experienced engineer, or solutions architect, this guide gives you the clarity, confidence, and tactical knowledge to thrive in the GenAI-driven future of software development. Armed with these insights, you’ll be ready to build, integrate, and scale intelligent solutions that enhance every stage of the software development lifecycle. What you will learn Build a secure GenAI application with expert guidance Understand the fundamentals of GenAI and its applications in software engineering Automate coding tasks with tools like GitHub Copilot, Amazon Q Developer, and OpenAI APIs Apply AI for debugging, testing, documentation, and deployment workflows Get to grips with prompt engineering and fine-tuning techniques to optimize AI outputs Implement best practices for architecting and scaling AI-powered applications Build end-to-end GenAI projects, moving from experimentation to production Who this book is for This book is for software developers, engineers, architects, and tech professionals who want to understand the core concepts of Generative AI and its real-world applications, master AI-driven development workflows to improve efficiency and code quality, and leverage tools like GitHub Copilot, Amazon Q Developer, and OpenAI APIs to automate coding tasks.

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. Design around the limitations of LLMs Ensure that generated content follows a specific style, tone, or format Maximize creativity while balancing different types of risk Build agents that plan, self-correct, take action, and collaborate with other agents Compose patterns into agentic applications for a variety of use cases

Practical techniques to accelerate software development using generative AI. Let’s get real. You’d like to hand off a lot of tedious software development tasks to an assistant—and now you can! AI-powered coding tools like Copilot can accelerate research, design, code creation, testing, troubleshooting, documentation, refactoring and more. Coding with AI shows you how. Written for working developers, this book fast-tracks you to AI-powered productivity with bite-size projects, tested prompts, and techniques for getting the most out of AI. In Coding with AI you’ll learn how to: Incorporate AI tools into your development workflow Create pro-quality documentation and tests Debug and refactor software efficiently Create and organize reusable prompts Coding with AI takes you through several small Python projects with the help of AI tools, showing you exactly how to use AI to create and refine real software. This book skips the baby steps and goes straight to the techniques you’ll use on the job, every day. You’ll learn to sidestep AI inefficiencies like hallucination and identify the places where AI can save you the most time and effort. About the Technology Taking a systematic approach to coding with Al will deliver the clarity, consistency, and scalability you need for production-grade applications. With practice, you can use AI tools to break down complex problems, generate maintainable code, enhance your models, and streamline debugging, testing, and collaboration. As you learn to work with AI’s strengths—and recognize its limitations—you’ll build more reliable software and find that the quality of your generated code improves significantly. About the Book Coding with AI shows you how to gain massive benefits from a powerful array of AI-driven development tools and techniques. And it shares the insights and methods you need to use them effectively in professional projects. Following realistic examples, you’ll learn AI coding for database integration, designing a UI, and establishing an automated testing suite. You’ll even vibe code a game—but only after you’ve built a rock-solid foundation. What's Inside Incorporate AI into your development workflow Create pro-quality documentation and tests Debug and refactor software efficiently Create and organize reusable prompts About the Reader For professional software developers. Examples in Python. About the Author Jeremy C. Morgan has two decades of experience as an engineer building software for everything from Fortune 100 companies to tiny startups. Quotes Delivers exactly what working developers need: practical techniques that actually work. - Scott Hanselman, Microsoft You’ll be writing prompt engineering poetry. - Lars Klint, Atlassian Blends years of software experience with hands-on knowledge of top AI coding techniques. Essential. - Steve Buchanan, Jamf Detailed use of AI in real-world applications. A great job! - Santosh Yadav, Celonis

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX! Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. In Deep Learning with Python, Third Edition you’ll discover: Deep learning from first principles The latest features of Keras 3 A primer on JAX, PyTorch, and TensorFlow Image classification and image segmentation Time series forecasting Large Language models Text classification and machine translation Text and image generation—build your own GPT and diffusion models! Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. About the Technology In less than a decade, deep learning has changed the world—twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. About the Book Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. What's Inside Hands-on, code-first learning Comprehensive, from basics to generative AI Intuitive and easy math explanations Examples in Keras, PyTorch, JAX, and TensorFlow About the Reader For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. About the Authors François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras. Quotes Perfect for anyone interested in learning by doing from one of the industry greats. - Anthony Goldbloom, Founder of Kaggle A sharp, deeply practical guide that teaches you how to think from first principles to build models that actually work. - Santiago Valdarrama, Founder of ml.school The most up-to-date and complete guide to deep learning you’ll find today! - Aran Komatsuzaki, EleutherAI Masterfully conveys the true essence of neural networks. A rare case in recent years of outstanding technical writing. - Salvatore Sanfilippo, Creator of Redis

Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today’s dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS) (NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translation AI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysis Cybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network security Social good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.