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Data is at the center of many challenges in system design today. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. In addition, there's an overwhelming variety of tools and analytical systems, including relational databases, NoSQL datastores, plus data warehouses and data lakes. What are the right choices for your application? How do you make sense of all these buzzwords?In this second edition, authors Martin Kleppmann and Chris Riccomini build on the foundation laid in the acclaimed first edition, integrating new technologies and emerging trends. You'll be guided through the maze of decisions and trade-offs involved in building a modern data system, from choosing the right tools like Spark and Flink to understanding the intricacies of data laws like the GDPR.Peer under the hood of the systems you already use, and learn to use them more effectivelyMake informed decisions by identifying the strengths and weaknesses of different toolsNavigate the trade-offs around consistency, scalability, fault tolerance, and complexityUnderstand the distributed systems research upon which modern databases are builtPeek behind the scenes of major online services, and learn from their architectures

Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.Understand what AI engineering is and how it differs from traditional machine learning engineeringLearn the process for developing an AI application, the challenges at each step, and approaches to address themExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they workExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome themChoose the right model, dataset, evaluation benchmarks, and metrics for your needsChip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).

In today's environment of partial attention and isolating remote work, few things are more satisfying than group experiences that produce powerful, meaningful connections and output. But this kind of enlivening, collective work doesn't happen by chance. It must be consciously designed and purposefully activated--;in a team, an organization, and a culture. The good news is that making space for this kind of work doesn't have to be hard. There's a technique available to everyone, with no special certification required: gamestorming.This substantial update to the best-selling O'Reilly book now includes three new chapters and 95 games. The authors identified and curated techniques from some of the world's most innovative professionals as well as inventing games of their own. This book is the result: a unique collection of approaches to simultaneously ignite engagement and level-up creative teamship while bringing agility and structure to gatherings at work in person and online.Shorten meetings and make them significantly more productiveIncrease efficacy and engagement in strategic thinking and problem-solvingEnhance connection and communication across team membersElevate collaboration and uncover surprising solutions to sticky problemsGenerate better ideas and significantly improve remote work experiences

Systems programming provides the foundation for the world's computation. Writing performance-sensitive code requires a programming language that puts programmers in control of how memory, processor time, and other system resources are used. The Rust systems programming language combines that control with a modern type system that catches broad classes of common mistakes, from memory management errors to data races between threads.With this practical guide, experienced systems programmers will learn how to successfully bridge the gap between performance and safety using Rust. Jim Blandy, Jason Orendorff, and Leonora Tindall demonstrate how Rust's features put programmers in control over memory consumption and processor use by combining predictable performance with memory safety and trustworthy concurrency.You'll learn:Rust's fundamental data types and the core concepts of ownership and borrowingHow to write flexible, efficient code with traits and genericsHow to write fast, multithreaded code without data racesRust's key power tools: closures, iterators, and asynchronous programmingCollections, strings and text, input and output, macros, unsafe code, and foreign function interfacesThis revised, updated edition covers the Rust 2021 Edition.

There are no easy decisions in software architecture. Instead, there are many hard parts--difficult problems or issues with no best practices--that force you to choose among various compromises. With this book, you'll learn how to think critically about the trade-offs involved with distributed architectures.Architecture veterans and practicing consultants Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani discuss strategies for choosing an appropriate architecture. By interweaving a story about a fictional group of technology professionals--the Sysops Squad--they examine everything from how to determine service granularity, manage workflows and orchestration, manage and decouple contracts, and manage distributed transactions to how to optimize operational characteristics, such as scalability, elasticity, and performance.By focusing on commonly asked questions, this book provides techniques to help you discover and weigh the trade-offs as you confront the issues you face as an architect.Analyze trade-offs and effectively document your decisionsMake better decisions regarding service granularityUnderstand the complexities of breaking apart monolithic applicationsManage and decouple contracts between servicesHandle data in a highly distributed architectureLearn patterns to manage workflow and transactions when breaking apart applications

This book is a guide to DevOps and software delivery: that is, a guide to the numerous tools and techniques that are required to take that application code and run it and maintain it in production, where it can generate value for your users and your company on an ongoing basis. This includes going through all the modern practices for deploying applications and microservices to the cloud, managing your infrastructure as code, automating your software delivery lifecycle in a CI/CD pipeline, configuring networking, setting up data stores, and hooking up monitoring.

We're building more products today than ever before, but most of them fail--not because we can't complete what we want to build but because we waste time, money, and effort building the wrong product. What we need is a systematic process for quickly vetting product ideas and raising our odds of success. That's the promise of Running Lean.In this inspiring book, Ash Maurya takes you through an exacting strategy for achieving product/market fit for your fledgling venture. You'll learn ideas and concepts from several innovative methodologies, including the Lean Startup, business model design, design thinking, and Jobs-to-be-Done. This new edition introduces the continuous innovation framework and follows one entrepreneur's journey from initial vision to a business model that works.Deconstruct your idea using a one-page Lean CanvasStress-test your idea for desirability, viability, and feasibilityDefine key milestones charted on a traction roadmapMaximize your team's efforts for speed, learning, and focusPrioritize the right actions at the right timeLearn how to conduct effective customer interviewsEngage your customers throughout the development cycleContinually test your product with smaller, faster iterationsFind a repeatable and scalable business model

Do you want to build web pages but have no prior experience? This friendly guide is the perfect place to start. You'll begin at square one, learning how the web and web pages work, and then steadily build from there. By the end of the book, you'll have the skills to create a simple site with multicolumn pages that adapt for mobile devices. Each chapter provides exercises to help you learn various techniques and short quizzes to make sure you understand key concepts. This thoroughly revised edition is ideal for students and professionals of all backgrounds and skill levels. It is simple and clear enough for beginners, yet thorough enough to be a useful reference for experienced developers keeping their skills up to date.

Messy code is a nuisance. "Tidying" code, to make it more readable, requires breaking it up into manageable sections. In this practical guide, author Kent Beck, creator of Extreme Programming and pioneer of software patterns, suggests when and where you might apply tidyings to improve your code while keeping the overall structure of the system in mind.Instead of trying to master tidying all at once, this book lets you try out a few examples that make sense for your problem. If you have a big function containing many lines of code, you'll learn how to logically divide it into smaller chunks. Along the way, you'll learn the theory behind software design: coupling, cohesion, discounted cash flows, and optionality.This book helps you:Understand the basic theory of how software design works and the forces that act on itExplore the difference between changes to a system's behavior and changes to its structureImprove your programming experience by sometimes tidying first and sometimes tidying afterLearn how to make large changes in small, safe stepsApproach design as a human activity with diverging incentives

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

To stay competitive in today’s market, organizations need to adopt a culture of customer-centric practices that focus on outcomes rather than outputs. Companies that live and die by outputs often fall into the "build trap," cranking out features to meet their schedule rather than the customer’s needs. In this book, Melissa Perri explains how laying the foundation for great product management can help companies solve real customer problems while achieving business goals. By understanding how to communicate and collaborate within a company structure, you can create a product culture that benefits both the business and the customer. You’ll learn product management principles that can be applied to any organization, big or small. In five parts, this book explores: Why organizations ship features rather than cultivate the value those features represent How to set up a product organization that scales How product strategy connects a company’s vision and economic outcomes back to the product activities How to identify and pursue the right opportunities for producing value through an iterative product framework How to build a culture focused on successful outcomes over outputs

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.Use scikit-learn to track an example machine learning project end to endExplore several models, including support vector machines, decision trees, random forests, and ensemble methodsExploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detectionDive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, and transformersUse TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learningTrain neural nets using multiple GPUs and deploy them at scale using Google's Vertex AI

AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.You'll learn how to use the power of pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pretrained models for text classification, search, and clusterings.This book also shows you how to:Build advanced LLM pipelines to cluster text documents and explore the topics they belong toBuild semantic search engines that go beyond keyword search with methods like dense retrieval and rerankersLearn various use cases where these models can provide valueUnderstand the architecture of underlying Transformer models like BERT and GPTGet a deeper understanding of how LLMs are trainedOptimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning

Salary surveys worldwide regularly place software architect in the top 10 best jobs, yet no real guide exists to help developers become architects. Until now. This updated edition provides a comprehensive overview of software architecture's many aspects, with five new chapters covering the latest insights from the field. Aspiring and existing architects alike will examine architectural characteristics, architectural patterns, component determination, diagramming architecture, governance, data, generative AI, team topologies, and many other topics.Mark Richards and Neal Ford-hands-on practitioners who have taught software architecture classes professionally for years-focus on architecture principles that apply across all technology stacks. You'll explore software architecture in a modern light, taking into account all the innovations of the past decade.This book examines:Architecture styles and patterns: Microservices, modular monoliths, microkernels, layered architectures, and many moreComponents: Identification, coupling, cohesion, partitioning, and granularitySoft skills: Effective team management, collaboration, business engagement models, negotiation, presentations, and moreModernity: Engineering practices and operational approaches that have changed radically in the past few years, including cloud considerations and generative AIArchitecture as an engineering discipline: Repeatable results, metrics, and concrete valuations that add rigor to software architecture

Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools. RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time. Work hands-on with three practical data analysis projects based on casino games Store, retrieve, and change data values in your computer's memory Write programs and simulations that outperform those written by typical R users Use R programming tools such as if else statements, for loops, and S3 classes Learn how to write lightning-fast vectorized R code Take advantage of R's package system and debugging tools Practice and apply R programming concepts as you learn them

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you will learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You will understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology. This book will help you: Assess data engineering problems using an end-to-end data framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle

Talking to people about your designs might seem like a basic skill, but it can be difficult to do well. In many cases, how you communicate with stakeholders, clients, and other nondesigners may be more important than the designs themselves. Because if you can’t get their support, your work will never see the light of day—no matter how good it is.This practical guide focuses on principles, tactics, and actionable methods for presenting your designs. Whether you design apps, websites, or products, you’ll learn how to get support from people who have influence over the project with the goal of creating the best user experience.Walk through the process of preparing and presenting your designsUnderstand stakeholder perspectives and learn how to empathize with themCultivate both implicit and explicit listening skillsLearn tactics and strategies for expressing the most effective response to feedbackCreate the right documentation for your decisions to avoid repeated conversationsLearn why following through is just as important as the meeting itself

Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you'll know it's time to move forward Apply Lean Analytics principles to large enterprises and established products

Many engineers assume their organization doesn't have an engineering strategy - when in fact, they often have one. It just may not be working. In Crafting Engineering Strategy, Will Larson (author of An Elegant Puzzle and Staff Engineer) offers a practical, example-rich guide to navigating technical and organizational complexity through structured, intentional strategy.Written for senior engineers, engineering leaders, architects, and curious collaborators, this book walks through a repeatable process for building effective, actionable strategies. With lessons drawn from real-world case studies at companies like Stripe, Uber, and Calm, Larson provides a framework for shaping critical decisions around system migrations, API deprecations, platform investments, and more. Along the way, you'll learn to bridge technical planning with communication, governance, and systems thinking. Whether you're shaping your team's direction or leading a company-wide initiative, this guide will help you make thoughtful decisions that stick.Build durable engineering strategies from first principlesApply methods like Wardley mapping and systems modelingLead strategy as a staff+ engineer or executiveLearn from detailed case studies across industryImprove your strategic fluency and influence over time

Product development is the magic that turns circuitry, software, and materials into a product, but moving efficiently from concept to manufactured product is a complex process with many potential pitfalls. This practical guide pulls back the curtain to reveal what happens-or should happen-when you take a product from prototype to production. For makers looking to go pro or product development team members keen to understand the process, author Alan Cohen tracks the development of an intelligent electronic device to explain the strategies and tactics necessary to transform an abstract idea into a successful product that people want to use.Learn 11 deadly sins that kill product development projects Get an overview of how electronic products are manufactured Determine whether your idea has a good chance of being profitable Narrow down the product's functionality and associated costs Generate requirements that describe the final product's details Select your processor, operating system, and power sources Learn how to comply with safety regulations and standards Dive into development-from rapid prototyping to manufacturing Alan Cohen, a veteran systems and software engineering manager and lifelong technophile, specializes in leading the development of medical devices and other high-reliability products. His passion is to work with engineers and other stakeholders to forge innovative technologies into successful products.

Ready to give your design skills a real boost? This eye-opening book helps you explore the design structure behind most of today's hit video games. You'll learn principles and practices for crafting games that generate emotionally charged experiences - a combination of elegant game mechanics, compelling fiction, and pace that fully immerses players. In clear and approachable prose, design pro Tynan Sylvester also looks at the day-to-day process necessary to keep your project on track, including how to work with a team, and how to avoid creative dead ends. Packed with examples, this book will change your perception of game design. Create game mechanics to trigger a range of emotions and provide a variety of play Explore several options for combining narrative with interactivity Build interactions that let multiplayer gamers get into each other's heads Motivate players through rewards that align with the rest of the game Establish a metaphor vocabulary to help players learn which design aspects are game mechanics Plan, test, and analyze your design through iteration rather than deciding everything up front Learn how your game's market positioning will affect your design

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated sixth edition will help you quickly write efficient, high-quality code with Python. It's an ideal way to begin, whether you're new to programming or a professional developer versed in other languages.Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow self-paced tutorial gets you started with Python 3.12 and all other releases in use today. With a pragmatic focus on what you need to know, it also introduces some advanced language features that have become increasingly common in Python code.This book helps you:Explore Python's built-in object types such as strings, lists, dictionaries, and filesCreate and process objects with Python statements, and learn Python's syntax modelUse functions and functional programming to avoid redundancy and maximize reuseOrganize code into larger components with modules and packagesCode robust programs with Python's exception handling and development toolsApply object-oriented programming and classes to make code customizableSurvey advanced Python tools including decorators, descriptors, and metaclassesWrite idiomatic Python code that runs portably across a wide variety of platforms

Coming to grips with C++11 and C++14 is more than a matter of familiarizing yourself with the features they introduce (e.g., auto type declarations, move semantics, lambda expressions, and concurrency support). The challenge is learning to use those features effectively - so that your software is correct, efficient, maintainable, and portable. That's where this practical book comes in. It describes how to write truly great software using C++11 and C++14 - i.e. using modern C++. Topics include: The pros and cons of braced initialization, noexcept specifications, perfect forwarding, and smart pointer make functions The relationships among std::move, std::forward, rvalue references, and universal references Techniques for writing clear, correct, effective lambda expressions How std::atomic differs from volatile, how each should be used, and how they relate to C++'s concurrency API How best practices in "old" C++ programming (i.e., C++98) require revision for software development in modern C++ Effective Modern C++ follows the proven guideline-based, example-driven format of Scott Meyers' earlier books, but covers entirely new material.

Three acknowledged experts in search engine optimization share guidelines and innovative techniques that will help you plan and execute a comprehensive SEO strategy. Complete with an array of effective tactics from basic to advanced, this fourth edition prepares digital marketers for 2022 and beyond with updates on SEO tools and new search engine optimization methods that have reshaped the SEO landscape. Novices will receive a thorough SEO education, while experienced SEO practitioners get an extensive reference to support ongoing engagements. Learn about the various intricacies and complexities of internet search Explore the underlying theory and inner workings of search engines and their algorithms Understand the interplay between social media engagement and other factors Discover tools to track results and measure success Examine the effects of key Google algorithm updates Consider opportunities for visibility in mobile, local, vertical, social, and voice search Build a competent SEO team with defined roles Gain insights into the future of search and internet discoverability

An understanding of psychology-specifically the psychology behind how users behave and interact with digital interfaces-is perhaps the single most valuable nondesign skill a designer can have. The most elegant design can fail if it forces users to conform to the design instead of working within the "blueprint" of how humans perceive and process the world around them.This practical guide explains how you can apply key principles of psychology to build products and experiences that are more human-centered and intuitive. Author Jon Yablonski deconstructs familiar apps and experiences to provide clear examples of how UX designers can build interfaces that adapt to how users perceive and process digital interfaces.You'll learn:How aesthetically pleasing design creates positive responsesThe principles of psychology most useful for designersHow these psychology principles relate to UX heuristicsPredictive models including Fitts's law, Jakob's law, and Hick's lawEthical implications of using psychology in designA practical framework for applying principles of psychology in your design processThis updated edition includes an even deeper connection to the underlying psychological concepts that govern the principles explored in the book, along with accompanying UX methods and techniques. Examples have been updated to ensure the deconstructed apps and experiences remain familiar and relevant.

Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse-a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way.Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools.You'll learn how to:Visualize-create plots for data exploration and communication of resultsTransform-discover types of variables and the tools you can use to work with themImport-get data into R and in a form convenient for analysisProgram-learn R tools for solving data problems with greater clarity and easeCommunicate-integrate prose, code, and results with Quarto

Prototyping and user testing is the best way to create successful products, but many designers skip this important step and use gut instinct instead. By explaining the goals and methodologies behind prototyping-and demonstrating how to prototype for both physical and digital products-this practical guide helps beginning and intermediate designers become more comfortable with creating and testing prototypes early and often in the process. Author Kathryn McElroy explains various prototyping methods, from fast and dirty to high fidelity and refined, and reveals ways to test your prototypes with users. You'll gain valuable insights for improving your product, whether it's a smartphone app or a new electronic gadget. Learn similarities and differences between prototyping for physical and digital products Know what fidelity level is needed for different prototypes Get best practices for prototyping in a variety of mediums, and choose which prototyping software or components to use Learn electronics prototyping basics and resources for getting started Write basic pseudocode and translate it into usable code for Arduino Conduct user tests to gain insights from prototypes

The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Perhaps you're ready to jump in, but you're unsure where or how to begin. 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.In a way that's approachable yet deeply informative, author Aurelien Geron delivers the ultimate introductory guide to machine learning and deep learning. With a focus on clear explanations and real-world Python examples, the book takes you through cutting-edge tools like scikit-learn and PyTorch-from basic regression techniques to advanced neural networks like transformers and generative adversarial networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to begin building intelligent systems.Understand ML basics, including concepts like overfitting and hyperparameter tuningLearn to build end-to-end ML projects using scikit-learn, from data exploration to model evaluationExplore advanced architectures like convolutional and recurrent neural networks with PyTorchDiscover techniques for unsupervised learning, such as clustering and anomaly detectionIncrease your expertise in state-of-the-art AI systems by fine-tuning pretrained modelsBuild tangible skills with complete hands-on coding exercises and real-world applications

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. While these applications enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks. By combining many of these actions, AI agents offer greater autonomy and efficiency, but understanding and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops.This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multi-agent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently. By the end, you'll:Understand the distinct features of foundation model-enabled AI agentsDiscover the core components and design principles of AI agentsExplore design trade-offs and implement effective multi-agent systemsDesign and deploy tailored AI solutions, enhancing efficiency and innovation in your field