Books published by O'Reilly Media

What do Docker, Kubernetes, and Prometheus have in common? All of these cloud native technologies are written in the Go programming language. This practical book shows you how to use Go's strengths to develop cloud native services that are scalable and resilient, even in an unpredictable environment.

Node.js is used by many companies for building performant backend services without sacrificing developer efficiency. In this hands-on guide, author Thomas Hunter II proves that Node.js is just as capable as traditional enterprise platforms for building services that are observable, scalable, and resilient.

Welcome to Make: 's 2020 Guide to Boards! This year brings powerful new releases from Adafruit, Arduino, BeagleBoard, Google, Nvidia, Raspberry Pi, Teensy, and more. We've assembled the technical data for new boards and returning favorites (listing over 50% more than our previous guide!), along with highlighting options to consider for your next project. To further help your decision-making process, some of our favorite electronics experts explain how they pick the right board for their projects. But the real star of electronic prototyping in 2020 is the software: In our cover story, we look at how Python-powered boards make it easier than ever to code for hardware. Plus, your favorite YouTube makers offer their tips and tricks for getting started making videos, how to grow your channel, and what you need to get a great shot (Hint: it's probably in your pocket right now). And don't forget, Halloween is right around the corner! Learn to build an R/C roving pop-up zombie-in-a-trashcan to scare the daylights out of the neighborhood, make a light and cheap fog projection screen for your haunted house, and create a flaming window setup so realistic, you'll have to warn the fire department about erroneous reports ahead of time. Lastly, read the finale of Make: 's series on how our community can help avert catastrophic climate change. Plus, over 43 projects including: Block ads across your entire home network with the Pi-hole ad blockerUse code to make beautiful topographical maps of the Moon, Mars, and moreCreate a swirling stormy snow globe with LED-lit rheoscopic fluidBuild a DIY mobile handwashing station for your communityTips and builds for exercising your constitutional right to protest safely and effectivelyMacrame an adorable Bay Yoda from The MandalorianAnd much more!

Data governance incorporates the ways people, processes, and technology work together to ensure data is trustworthy and can be used effectively. This practical guide shows you how to effectively implement and scale data governance throughout your organization.

Targeted at developers and architects, this book presents a framework through examples, practical advice, and use cases to help you design and automate complex processes.

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcementand enable a machine to learn by itself.Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learnnumerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML.Learn what RL is and how the algorithms help solve problemsBecome grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learningDive deep into a range of value and policy gradient methodsApply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learningUnderstand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and moreGet practical examples through the accompanying website

Save time and trouble when using Scala to build object-oriented, functional, and concurrent applications. With more than 250 ready-to-use recipes and 700 code examples, this comprehensive cookbook covers the most common problems you'll encounter when using the Scala language, libraries, and tools.

Site reliability engineering (SRE) is more relevant than ever. Knowing how to keep systems reliable has become a critical skill. With this practical book, newcomers and old hats alike will explore a broad range of conversations happening in SRE.

With this book, professionals from around the world provide valuable insight into today's cloud engineering role. These concise articles explore the entire cloud computing experience, including fundamentals, architecture, and migration.

Working with AI is complicated and expensive for many developers. That's why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video.Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quickly get up to speed with Google Cloud Platform, whether you want to build an AI assistant or a simple business AI application.Learn key concepts for data science, machine learning, and deep learningExplore tools like Video AI and AutoML TablesBuild a simple language processor using deep learning systemsPerform image recognition using CNNs, transfer learning, and GANsUse Google's Dialogflow to create chatbots and conversational AIAnalyze video with automatic video indexing, face detection, and TensorFlow HubBuild a complete working AI agent application

Making significant changes to large, complex codebases is a daunting task--one that's nearly impossible to do successfully unless you have the right team, tools, and mindset. If your application is in need of a substantial overhaul and you're unsure how to go about implementing those changes in a sustainable way, then this book is for you.Software engineer Maude Lemaire walks you through the entire refactoring process from start to finish. You'll learn from her experience driving performance and refactoring efforts at Slack during a period of critical growth, including two case studies illustrating the impact these techniques can have in the real world. This book will help you achieve a newfound ability to productively introduce important changes in your codebase.Understand how code degrades and why some degradation is inevitableQuantify and qualify the state of your codebase before refactoringDraft a well-scoped execution plan with strategic milestonesWin support from engineering leadershipBuild and coordinate a team best suited for the projectCommunicate effectively inside and outside your teamAdopt best practices for successfully executing the refactor

This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.

Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data.

Accessible and fun to read, this practical book contains a collection of stories of organizations using blockchain technology in practice. Through deep research and firsthand interviews, authors Sir John Hargrave and Evan Karnoupakis show you how leading-edge organizations have worked to integrate blockchain into their businesses.

Microservices architectures offer faster change speeds, better scalability, and cleaner, evolvable system designs. With this book, authors Ronnie Mitra and Irakli Nadareishvili provide step-by-step guidance for building an effective microservices architecture.

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. Youll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. Youll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples.This book covers:Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio managementSupervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategiesDimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve constructionAlgorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio managementReinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio managementNLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Whether your company is considering serverless computing or has already made the decision to adopt this model, this practical book is for you. Author Jason Katzer shows early- and mid-career developers what's required to build and ship maintainable and scalable services using this model.

Problem solving with JavaScript is a lot trickier now that its use has expanded considerably in size, scope, and complexity. This cookbook has your back, with recipes for common tasks across the JavaScript world, whether youre working in the browser, the server, or a mobile environment. Each recipe includes reusable code and practical advice for tackling JavaScript objects, Node, Ajax, JSON, data persistence, graphical and media applications, complex frameworks, modular JavaScript, APIs, and many related technologies.Aimed at people who have some experience with JavaScript, the first part covers traditional uses of JavaScript, along with new ideas and improved functionality. The second part dives into the server, mobile development, and a plethora of leading-edge tools. Youll save timeand learn more about JavaScript in the process.Topics include:Classic JavaScript:Arrays, functions, and the JavaScript ObjectAccessing the user interfaceTesting and accessibilityCreating and using JavaScript librariesClient-server communication with AjaxRich, interactive web effectsJavaScript, All Blown Up:New ECMAScript standard objectsUsing Node on the serverModularizing and managing JavaScriptComplex JavaScript frameworksAdvanced client-server communicationsVisualizations and client-server graphicsMobile application development

What value does semantic data modeling offer? As an information architect or data science professional, lets say you have an abundance of the right data and the technology to extract business goldbut you still fail. The reason? Bad data semantics.In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. Youll learn how to master this craft to increase the usability and value of your data and applications. Youll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data.Understand the fundamental concepts, phenomena, and processes related to semantic data modelingExamine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and toolsAvoid mistakes and bad practices that can undermine your efforts to create good data modelsLearn about model development dilemmas, including representation, expressiveness and content, development, and governanceOrganize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges

This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable

The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading.Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book.In five parts, this guide helps you:Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI)Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practiceApply neural networks and reinforcement learning to discover statistical inefficiencies in financial marketsIdentify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategiesUnderstand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about

Although service-level objectives (SLOs) continue to grow in importance, theres a distinct lack of information about how to implement them. Practical advice that does exist usually assumes that your team already has the infrastructure, tooling, and culture in place. In this book, recognized SLO expert Alex Hidalgo explains how to build an SLO culture from the ground up.Ideal as a primer and daily reference for anyone creating both the culture and tooling necessary for SLO-based approaches to reliability, this guide provides detailed analysis of advanced SLO and service-level indicator (SLI) techniques. Armed with mathematical models and statistical knowledge to help you get the most out of an SLO-based approach, youll learn how to build systems capable of measuring meaningful SLIs with buy-in across all departments of your organization.Define SLIs that meaningfully measure the reliability of a service from a users perspectiveChoose appropriate SLO targets, including how to perform statistical and probabilistic analysisUse error budgets to help your team have better discussions and make better data-driven decisionsBuild supportive tooling and resources required for an SLO-based approachUse SLO data to present meaningful reports to leadership and your users