Böcker utgivna av O'Reilly Media

With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development.You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step.Learn the fundamentals of cloud native applicationsExplore key cloud native communication, connectivity, and composition patternsLearn decentralized data management techniquesUse event-driven architecture to build distributed and scalable cloud native applicationsExplore the most commonly used patterns for API management and consumptionExamine some of the tools and technologies you'll need for building cloud native systems

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis.Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately.Understand the specifics of behavioral dataExplore the differences between measurement and predictionLearn how to clean and prepare behavioral dataDesign and analyze experiments to drive optimal business decisionsUse behavioral data to understand and measure cause and effectSegment customers in a transparent and insightful way

Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained--usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software.Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and tools??including linters, formatters, type checkers, and tests??to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming.Create command-line Python programs to document and validate parametersWrite tests to verify refactor programs and confirm they're correctAddress bioinformatics ideas using Python data structures and modules such as BiopythonCreate reproducible shortcuts and workflows using makefilesParse essential bioinformatics file formats such as FASTA and FASTQFind patterns of text using regular expressionsUse higher-order functions in Python like filter(), map(), and reduce()

The book is written in a casual, conversational style. It is easily accessible to those who have no prior knowledge in 3D printing, yet the book's message is solidly practical, technically accurate, and consumer-relevant. The chapters include contemporary, real-life learning exercises and insights for how to buy, use and maintain 3D printers.

Digital fabrication is a cornerstone of the maker community, and in Make: Volume 75 (our diamond issue!), we take a fun look at the state of digifab today. In our cover story, we break out of the cartesian box with a look at Nick Sewardâ??s weird and wild 3D printer designs, from undulating arms to roving printers. Next, dive into the latest desktop mills, routers and 3D printers with reviews of machines from Bantam Tools, Prusa Research, and more. Plus, get tips for recycling your old prints, adding supports for resin prints, printing in clay, and more.Plus, over 16 projects and skill builders, including:Fit real AI onto microcontroller with TinyML and ArduinoBuild a super efficient rocket stove out of tin cans for your next camping tripBeam streaming services to your TV using a Raspberry Pi 4Create an adorable light-up felted mushroom gardenAnd much more!

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.

Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public. You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub.Hands-On Data Visualization takes you step-by-step through tutorials, real-world examples, and online resources. This practical guide is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required.Build interactive charts and maps and embed them in your websiteUnderstand the principles for designing effective charts and mapsLearn key data visualization concepts to help you choose the right toolsConvert and transform tabular and spatial data to tell your data storyEdit and host Chart.js, Highcharts, and Leaflet map code templates on GitHubLearn how to detect bias in charts and maps produced by others

Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.Dive into Kubeflow architecture and learn best practices for using the platformUnderstand the process of planning your Kubeflow deploymentInstall Kubeflow on an existing on-premises Kubernetes clusterDeploy Kubeflow on Google Cloud Platform step-by-step from the command lineUse the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWSDeploy and manage Kubeflow across a network of Azure cloud data centers around the worldUse KFServing to develop and deploy machine learning models

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!

Tap into the wisdom of experts to learn what every UX practitioner needs to know. With 97 short and extremely useful articles, you'll discover new approaches to old problems, pick up road-tested best practices, and hone your skills through sound advice.Working in UX involves much more than just creating user interfaces. UX teams struggle with understanding what's important, which practices they should know deeply, and what approaches aren't helpful at all. With these 97 concise articles, editor Dan Berlin presents a wealth of advice and knowledge from experts who have practiced UX throughout their careers.Bring Themes to Exploratory Research--Shanti KanhaiDesign for Content First--Marli MesibovDesign for Universal Usability--Ann Chadwick-DiasBe Wrong on Purpose--Skyler Ray TaylorDiverse Participant Recruiting Is Critical to Authentic User Research--Megan CamposPut On Your InfoSec Hat to Improve Your Designs--Julie MeridianBoost Your Emotional Intelligence to Move from Good to Great UX--Priyama Barua

This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With some Python experience and a basic understanding of machine learning, you'll learn how to build and deploy real-world NLP applications in your organization.

As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets compliance requirements. 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.Chief information, data, and security officers and their teams will learn strategy and tooling to support democratizing data and unlocking its value while enforcing security, privacy, and other governance standards. Through good data governance, you can inspire customer trust, enable your organization to identify business efficiencies, generate more competitive offerings, and improve customer experience. This book shows you how.You'll learn:Data governance strategies addressing people, processes, and toolsBenefits and challenges of a cloud-based data governance approachHow data governance is conducted from ingest to preparation and useHow to handle the ongoing improvement of data qualityChallenges and techniques in governing streaming dataData protection for authentication, security, backup, and monitoringHow to build a data culture in your organization

In today??s IT architectures, microservices and serverless functions play increasingly important roles in process automation. But how do you create meaningful, comprehensive, and connected business solutions when the individual components are decoupled and independent by design? 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.As systems are more distributed, asynchronous, and reactive, process automation requires state handling to deal with long-running interactions. Author Bernd Ruecker demonstrates how to leverage process automation technology like workflow engines to orchestrate software, humans, decisions, or bots.Learn how modern process automation compares to business process management, service-oriented architecture, batch processing, event streaming, and data pipeline solutionsUnderstand how to use workflow engines and executable process models with BPMNUnderstand the difference between orchestration and choreography and how to balance both

Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time.Mitch Seymour, data services engineer at Mailchimp, explains important stream processing concepts against a backdrop of several interesting business problems. You'll learn the strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing.Learn the basics of Kafka and the pub/sub communication patternBuild stateless and stateful stream processing applications using Kafka Streams and ksqlDBPerform advanced stateful operations, including windowed joins and aggregationsUnderstand how stateful processing works under the hoodLearn about ksqlDB's data integration features, powered by Kafka ConnectWork with different types of collections in ksqlDB and perform push and pull queriesDeploy your Kafka Streams and ksqlDB applications to production

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

If you're grounded in the basics of Swift, Xcode, and the Cocoa framework, this book provides a structured explanation of all essential real-world iOS app components. Through deep exploration and copious code examples, you'll learn how to create views, manipulate view controllers, and add features from iOS frameworks.

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.This book helps you:Fulfill data science value by reducing friction throughout ML pipelines and workflowsRefine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracyDesign the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainableOperationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

This practical book is one of the first to describe present and future use cases where AI can help solve pernicious healthcare problems. Kerrie Holley and Siupo Becker provide guidance to help informatics and healthcare leadership create AI strategy and implementation plans for healthcare.

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.

Alongside its popular web server, NGINX provides a dynamic application server that supports configuration through a RESTful JSON API. The open source NGINX Unit server deploys configuration changes without service disruptions and runs apps built with multiple languages and frameworks. This updated cookbook shows developers, DevOps personnel, network admins, and cloud infrastructure pros how to quickly get started with NGINX Unit.Hands-on recipes demonstrate Units new approach and show you how to deploy and configure this server for different applications. Youll learn how to run applications written in different languages on the same server, how to use NGINX Unit as the foundation for your web application development environment, and how Units RESTful API simplifies configuration.Learn how Unit differs from other middleware application serversInstall Unit using source code, Red Hat and Debian systems, or third-party repositoriesConfigure Unit using application, router, and listener objectsStart and stop the Unit server and the applications it runsManage user permissions, Linux namespace isolation, and API securityRun WordPress, Django, and other web applications with UnitServe applications with an NGINX proxy or load balancer

The Covid-19 crisis has been a defining moment for the maker movement. Groups and individuals are designing and producing personal protective equipment like face shields and masks, forming grassroots

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. You'll get actionable advice on several topics, including how to adopt SRE, why SLOs matter, when you need to upgrade your incident response, and how monitoring and observability differ.Editors Jaime Woo and Emil Stolarsky, co-founders of Incident Labs, have collected 97 concise and useful tips from across the industry, including trusted best practices and new approaches to knotty problems. You'll grow and refine your SRE skills through sound advice and thought-provokingquestions that drive the direction of the field.Some of the 97 things you should know:"e;Test Your Disaster Plan"e;--Tanya Reilly"e;Integrating Empathy into SRE Tools"e;--Daniella Niyonkuru"e;The Best Advice I Can Give to Teams"e;--Nicole Forsgren"e;Where to SRE"e;--Fatema Boxwala"e;Facing That First Page"e;--Andrew Louis"e;I Have an Error Budget, Now What?"e;--Alex Hidalgo"e;Get Your Work Recognized: Write a Brag Document"e;--Julia Evans and Karla Burnett

If you create, manage, operate, or configure systems running in the cloud, you're a cloud engineer--even if you work as a system administrator, software developer, data scientist, or site reliability engineer. 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. You'll delve into security and compliance, operations and reliability, and software development. And examine networking, organizational culture, and more. You're sure to find 1, 2, or 97 things that inspire you to dig deeper and expand your own career."e;Three Keys to Making the Right Multicloud Decisions,"e; Brendan O'Leary"e;Serverless Bad Practices,"e; Manases Jesus Galindo Bello"e;Failing a Cloud Migration,"e; Lee Atchison"e;Treat Your Cloud Environment as If It Were On Premises,"e; Iyana Garry"e;What Is Toil, and Why Are SREs Obsessed with It?"e;, Zachary Nickens"e;Lean QA: The QA Evolving in the DevOps World,"e; Theresa Neate"e;How Economies of Scale Work in the Cloud,"e; Jon Moore"e;The Cloud Is Not About the Cloud,"e; Ken Corless"e;Data Gravity: The Importance of Data Management in the Cloud,"e; Geoff Hughes"e;Even in the Cloud, the Network Is the Foundation,"e; David Murray"e;Cloud Engineering Is About Culture, Not Containers,"e; Holly Cummins

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

Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.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.Extract data from APIs and web pagesPrepare textual data for statistical analysis and machine learningUse machine learning for classification, topic modeling, and summarizationExplain AI models and classification resultsExplore and visualize semantic similarities with word embeddingsIdentify customer sentiment in product reviewsCreate a knowledge graph based on named entities and their relations

With more and more companies moving on-premises applications to the cloud, software and cloud solution architects alike are busy investigating ways to improve load balancing, performance, security, and high availability for workloads. This practical book describes Microsoft Azure's load balancing options and explains how NGINX can contribute to a comprehensive solution.Cloud architects Derek DeJonghe and Arlan Nugara take you through the steps necessary to design a practical solution for your network. Software developers and technical managers will learn how these technologies have a direct impact on application development and architecture. While the examples are specific to Azure, these load balancing concepts and implementations also apply to cloud providers such as AWS, Google Cloud, DigitalOcean, and IBM Cloud.Understand application delivery and load balancing--and why they're importantExplore Azure's managed load balancing optionsLearn how to run NGINX OSS and NGINX Plus on AzureExamine similarities and complementing features between Azure-managed solutions and NGINXUse Azure Front Door to define, manage, and monitor global routing for your web trafficMonitor application performance using Azure and NGINX tools and plug-insExplore security choices using NGINX and Azure Firewall solutions

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.You'll start by exploring the origins of blockchain, with plain-English descriptions of industry terminology like bitcoin, cryptocurrencies, and smart contracts. Then you'll dive into 10 story-driven case studies that will teach you easy-to-understand blockchain best practices.Explore real-life examples of companies developing and integrating blockchain applications for mobile voting, credentialing, supply chains, and a $100 million virtual cat collectible marketplaceDiscover how blockchain is transforming industries like banking, communications, government, logistics, and nonprofitsLearn about engaging blockchain success stories, such as Binance, Ethereum, and CircleExamine common blockchain best practices, with illustrations for easy reference, and learn how to apply them in your business, government project, or charitable foundation