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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

Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms.There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.

Software development today is embracing events and streaming data, which optimizes not only how technology interacts but also how businesses integrate with one another to meet customer needs. This phenomenon, called flow, consists of patterns and standards that determine which activity and related data is communicated between parties over the internet.This book explores critical implications of that evolution: What happens when events and data streams help you discover new activity sources to enhance existing businesses or drive new markets? What technologies and architectural patterns can position your company for opportunities enabled by flow? James Urquhart, global field CTO at VMware, guides enterprise architects, software developers, and product managers through the process.Learn the benefits of flow dynamics when businesses, governments, and other institutions integrate via events and data streamsUnderstand the value chain for flow integration through Wardley mapping visualization and promise theory modelingWalk through basic concepts behind today's event-driven systems marketplaceLearn how today's integration patterns will influence the real-time events flow in the futureExplore why companies should architect and build software today to take advantage of flow in coming years

InfoSec professionals frequently struggle to integrate security into their companies' processes. Many are at odds with their organizations. Most are under-resourced. There must be a better way. This essential manager's guide offers a new approach to building and maintaining an information security program that's both effective and easy to follow.

Enterprise developers face several challenges when it comes to building serverless applications, such as integrating applications and building container images from source. With more than 60 practical recipes, this cookbook helps you solve these issues with Knativethe first serverless platform natively designed for Kubernetes. Each recipe contains detailed examples and exercises, along with a discussion of how and why it works.If you have a good understanding of serverless computing and Kubernetes core resources such as deployment, services, routes, and replicas, the recipes in this cookbook show you how to apply Knative in real enterprise application development. Authors Kamesh Sampath and Burr Sutter include chapters on autoscaling, build and eventing, observability, Knative on OpenShift, and more.With this cookbook, youll learn how to:Efficiently build, deploy, and manage modern serverless workloadsApply Knative in real enterprise scenarios, including advanced eventingMonitor your Knative serverless applications effectivelyIntegrate Knative with CI/CD principles, such as using pipelines for faster, more successful production deploymentsDeploy a rich ecosystem of enterprise integration patterns and connectors in Apache Camel K as Kubernetes and Knative components

Learn how to build a real-world serverless application in the cloud that's reliable, secure, maintainable, and scalable. If you have experience building web applications on traditional infrastructure, this hands-on guide shows you how to get started with Cloud Run, a container-based serverless product on Google Cloud.Through the course of this book, you'll learn how to deploy several example applications that highlight different parts of the serverless stack on Google Cloud. Combining practical examples with fundamentals, this book will appeal to developers who are early in their learning journey as well as experienced practitioners.Build a serverless application with Google Cloud RunLearn approaches for building containers with (and without) DockerExplore Google Cloud's managed relational database: Cloud SQLUse HTTP sessions to make every user's experience uniqueExplore identity and access management (IAM) on Cloud RunProvision Google Cloud resources using TerraformLearn how to handle background task scheduling on Cloud RunMove your service from Cloud Run to Knative Serving with little effort

While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs.Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. Youll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.Break business decisions into stages that can be tackled using different skills from the analytical toolboxIdentify and embrace uncertainty in decision making and protect against common human biasesCustomize optimal decisions to different customers using predictive and prescriptive methods and technologiesAsk business questions that create high value through AI- and data-driven technologies

Level up with Tableau to build eye-catching, easy-to-interpret data visualizations. In this follow-up guide to Practical Tableau, author Ryan Sleeper takes you through a collection of unique tips and tutorials for using this popular software. Beginning to advanced Tableau users will learn how to go beyond Show Me to make better charts and learn dozens of tricks to improve both the author and user experience.Featuring many approaches he developed himself, Ryan shows you how to create charts that empower Tableau users to explore, understand, and derive value from their data. He also shares many of his favorite tricks that enabled him to become a Tableau Zen Master, Tableau Public Visualization of the Year author, and Tableau Global Iron Viz Champion.Learn whats new in Tableau since Practical Tableau was releasedExamine unique new chartstimelines, custom gauges, and leapfrog chartsplus innovations to traditional charts such as highlight tables, scatter plots, and mapsGet tips that can help make a Tableau developers life easierUnderstand what developers can do to make users lives easier

Bring agility, cost savings, and a competitive edge to your business by migrating your IT infrastructure to AWS. With this practical book, executive and senior leadership and engineering and IT managers will examine the advantages, disadvantages, and common pitfalls when moving your companys operations to the cloud.Author Jeff Armstrong brings years of practical hands-on experience helping dozens of enterprises make this corporate change. Youll explore real-world examples from many organizations that have madeor attempted to makethis wide-ranging transition. Once you read this guide, youll be better prepared to evaluate your migration objectively before, during, and after the process in order to ensure success.Learn the benefits and drawbacks of migrating to AWS, including the risks to your business and technologyBegin the process by discovering the applications and servers in your environmentExamine the value of AWS migration when building your business caseAddress your operational readiness before you migrateDefine your AWS account structure and cloud governance controlsCreate your migration plan in waves of servers and applicationsRefactor applications that will benefit from using more cloud native resources

Most of the high-profile cases of real or perceived unethical activity in data science arent matters of bad intent. Rather, they occur because the ethics simply arent thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult.In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices.Articles include:Ethics Is Not a Binary ConceptTim WilsonHow to Approach Ethical TransparencyRado KotorovUnbiased FairDoug HagueRules and RationalityChristof Wolf BrennerThe Truth About AI BiasCassie KozyrkovCautionary Ethics TalesSherrill HayesFairness in the Age of AlgorithmsAnna JacobsonThe Ethical Data StorytellerBrent DykesIntroducing Ethicize, the Fully AI-Driven Cloud-Based Ethics Solution!Brian ONeillBe Careful with "e;Decisions of the Heart"e;Hugh WatsonUnderstanding Passive Versus Proactive EthicsBill Schmarzo

Authors Alex Soto Bueno and Jason Porter from Red Hat provide detailed solutions for installing, interacting with, and using Quarkus in the development and production of microservices. The recipes in this book show midlevel to senior developers familiar with Java enterprise application development how to get started with Quarkus quickly.

Organizations today often struggle to balance business requirements with ever-increasing volumes of data. Additionally, the demand for leveraging large-scale, real-time data is growing rapidly among the most competitive digital industries. Conventional system architectures may not be up to the task. With this practical guide, youll learn how to leverage large-scale data usage across the business units in your organization using the principles of event-driven microservices.Author Adam Bellemare takes you through the process of building an event-driven microservice-powered organization. Youll reconsider how data is produced, accessed, and propagated across your organization. Learn powerful yet simple patterns for unlocking the value of this data. Incorporate event-driven design and architectural principles into your own systems. And completely rethink how your organization delivers value by unlocking near-real-time access to data at scale.Youll learn:How to leverage event-driven architectures to deliver exceptional business valueThe role of microservices in supporting event-driven designsArchitectural patterns to ensure success both within and between teams in your organizationApplication patterns for developing powerful event-driven microservicesComponents and tooling required to get your microservice ecosystem off the ground

Companies are spending billions on machine learning projects, but its money wasted if the models cant be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. Youll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques

If you want to build an enterprise-quality application that uses natural language text but arent sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language processing (NLP) applications using deep learning and the Apache Spark NLP library.Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. Youll also explore special concerns for developing text-based applications, such as performance.In four sections, youll learn NLP basics and building blocks before diving into application and system building:Basics: Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learningBuilding blocks: Learn techniques for building NLP applicationsincluding tokenization, sentence segmentation, and named-entity recognitionand discover how and why they workApplications: Explore the design, development, and experimentation process for building your own NLP applicationsBuilding NLP systems: Consider options for productionizing and deploying NLP models, including which human languages to support

Climate change is an urgent threat but if we makers act now, we can still make a difference at both micro and macro levels. Our cover story, the kickoff to a series that will run through 2020, gives a big-picture look at what steps we can take to arrest climate change. Hint: It starts by electrifying everything!Then, we look at some tasty tech with the story of a seriously impressive cheeseburger-making robot, recipes to cook delicious insects, and instructions to build a Raspberry Pi-powered cocktail dispenser for your next BBQ or robot-themed party.Plus, 21 projects to make, including:Hack the Sonos-Ikea Symfonisk to make high-quality, networked bookshelf speakers on a budgetBuild the world's newest, simplest siege weapon, the Walking Arm TrebuchetFold and fly the Guinness World Record paper airplaneMake a jig for quick, easy, and beautiful box jointsOur best-yet DIY coffee bean roasterAnd much more!

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic datafake data generated from real dataso you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue.Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution.This book describes:Steps for generating synthetic data using multivariate normal distributionsMethods for distribution fitting covering different goodness-of-fit metricsHow to replicate the simple structure of original dataAn approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure

Every day, companies struggle to scale critical applications. As traffic volume and data demands increase, these applications become more complicated and brittle, exposing risks and compromising availability. With the popularity of software as a service, scaling has never been more important.Updated with an expanded focus on modern architecture paradigms such as microservices and cloud computing, this practical guide provides techniques for building systems that can handle huge quantities of traffic, data, and demandwithout affecting the quality your customers expect. Architects, managers, and directors in engineering and operations organizations will learn how to build applications at scale that run more smoothly and reliably to meet the needs of customers.Learn how scaling affects the availability of your services, why that matters, and how to improve itDive into a modern service-based application architecture that ensures high availability and reduces the effects of service failuresExplore the Single Team Owned Service Architecture paradigm (STOSA)a model for scaling your development organization in tandem with your applicationUnderstand, measure, and mitigate risk in your systemsUse the cloud to build highly scalable applications

With this practical guide, business leaders will discover where they are in their AI journey and learn the steps necessary to successfully scale AI throughout their organization.

Java continues to grow and evolve, and this cookbook continues to evolve in tandem. With this guide, youll get up to speed right away with hundreds of hands-on recipes across a broad range of Java topics. Youll learn useful techniques for everything from string handling and functional programming to network communication.Each recipe includes self-contained code solutions that you can freely use, along with a discussion of how and why they work. If youre familiar with Java basics, this cookbook will bolster your knowledge of the language and its many recent changes, including how to apply them in your day-to-day development. This updated edition covers changes through Java 12 and parts of 13 and 14.Recipes include:Methods for compiling, running, and debuggingPackaging Java classes and building applicationsManipulating, comparing, and rearranging textRegular expressions for string and pattern matchingHandling numbers, dates, and timesStructuring data with collections, arrays, and other typesObject-oriented and functional programming techniquesInput/output, directory, and filesystem operationsNetwork programming on both client and serverProcessing JSON for data interchangeMultithreading and concurrencyUsing Java in big data applicationsInterfacing Java with other languages

How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner.Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing device and IoT data, based on collection models and use cases that address real business needs. These examples come from some of the most demanding data environments, such as healthcare, using approaches that have withstood the test of time.Create anonymization solutions diverse enough to cover a spectrum of use casesMatch your solutions to the data you use, the people you share it with, and your analysis goalsBuild anonymization pipelines around various data collection models to cover different business needsGenerate an anonymized version of original data or use an analytics platform to generate anonymized outputsExamine the ethical issues around the use of anonymized data

Threat modeling is one of the most essential--and most misunderstood--parts of the development lifecycle. Whether you're a security practitioner or a member of a development team, this book will help you gain a better understanding of how you can apply core threat modeling concepts to your practice to protect your systems against threats.Contrary to popular belief, threat modeling doesn't require advanced security knowledge to initiate or a Herculean effort to sustain. But it is critical for spotting and addressing potential concerns in a cost-effective way before the code's written--and before it's too late to find a solution. Authors Izar Tarandach and Matthew Coles walk you through various ways to approach and execute threat modeling in your organization.Explore fundamental properties and mechanisms for securing data and system functionalityUnderstand the relationship between security, privacy, and safetyIdentify key characteristics for assessing system securityGet an in-depth review of popular and specialized techniques for modeling and analyzing your systemsView the future of threat modeling and Agile development methodologies, including DevOps automationFind answers to frequently asked questions, including how to avoid common threat modeling pitfalls

Make: magazine is back in action and back to our original size! This issue's cover project is a maker's take on a Boston Dynamics-style quadrupedal walking robot that you can build yourself. Then, build an adorable unicorn shaped dispenser that spits soap on command. And to celebrate Make's return, why not build a custom dancing version of our Makey mascot.Plus, 28 projects including:Teeny-tiny personal motorboatStandup paddle boardBird-identifying computer-vision birdhouseBackyard bicycle pump trackAnd much more!

How can startups successfully scale customer acquisition and revenue growth with a Lean team? Out-of-the-box acquisition solutions from Facebook, Google, and others provide a good start, but the companies that can tailor those solutions to meet their specific needs, objectives, and goals will come out winners. But that hasnt been an easy taskuntil now.With this practical book, author Lomit Patel shows you how to use AI and automation to provide an operational layer atop those acquisition solutions to deliver amazing results for your company. Youll learn how to adapt, customize, and personalize cross-channel user journeys to help your company attract and retain customersto usher in the new age of Autonomous Marketing.Learn how AI and automation can support the customer acquisition efforts of a Lean StartupDive into Customer Acquisition 3.0, an initiative for gaining and retaining customersExplore ways to use AI for marketing purposesUnderstand the key metrics for determining the growth of your startupDetermine the right strategy to foster user acquisition in your companyManage the increased complexity and risk inherent in AI projects

Building a culture is just like hacking your way to a successful product. This practical book provides building blocks, practical tips, and inspiring stories from some of the most successful companies in the world to highlight the power of connecting with your customers.

To facilitate scalability and resilience, many organizations now run applications in cloud native environments using containers and orchestration. But how do you know if the deployment is secure? This practical book examines key underlying technologies to help developers, operators, and security professionals assess security risks and determine appropriate solutions.Author Liz Rice, VP of open source engineering at Aqua Security, looks at how the building blocks commonly used in container-based systems are constructed in Linux. Youll understand whats happening when you deploy containers and learn how to assess potential security risks that could affect your deployments. If you run container applications with kubectl or docker and use Linux command-line tools such as ps and grep, youre ready to get started.Explore attack vectors that affect container deploymentsDive into the Linux constructs that underpin containersExamine measures for hardening containersUnderstand how misconfigurations can compromise container isolationLearn best practices for building container imagesIdentify container images that have known software vulnerabilitiesLeverage secure connections between containersUse security tooling to prevent attacks on your deployment

If youre among the Python developers put off by asyncios complexity, its time to take another look. Asyncio is complicated because it aims to solve problems in concurrent network programming for both framework and end-user developers. The features you need to consider are a small subset of the whole asyncio API, but picking out the right features is the tricky part. Thats where this practical book comes in.Veteran Python developer Caleb Hattingh helps you gain a basic understanding of asyncios building blocksenough to get started writing simple event-based programs. Youll learn why asyncio offers a safer alternative to preemptive multitasking (threading) and how this API provides a simpleway to support thousands of simultaneous socket connections.Get a critical comparison of asyncio and threading for concurrent network programmingTake an asyncio walk-through, including a quickstart guidefor hitting the ground looping with event-based programmingLearn the difference between asyncio features for end-user developers and those for framework developersUnderstand asyncios new async/await language syntax, including coroutines and task and future APIsGet detailed case studies (with code) of some popular asyncio-compatible third-party libraries

Coding and testing are generally considered separate areas of expertise. In this practical book, Java expert Scott Oaks takes the approach that anyone who works with Java should be adept at understanding how code behaves in the Java Virtual Machineincluding the tunings likely to help performance. This updated second edition helps you gain in-depth knowledge of Java application performance using both the JVM and the Java platform.Developers and performance engineers alike will learn a variety of features, tools, and processes for improving the way the Java 8 and 11 LTS releases perform. While the emphasis is on production-supported releases and features, this book also features previews of exciting new technologies such as ahead-of-time compilation and experimental garbage collections.Understand how various Java platforms and compilers affect performanceLearn how Java garbage collection worksApply four principles to obtain best results from performance testingUse the JDK and other tools to learn how a Java application is performingMinimize the garbage collectors impact through tuning and programming practicesTackle performance issues in Java APIsImprove Java-driven database application performance

Most applications today are distributed in some fashion. Monitoring the health and performance of these distributed architectures requires a new approach. Enter distributed tracing, a method of profiling and monitoring applicationsespecially those that use microservice architectures. Theres just one problem: distributed tracing can be hard. But it doesnt have to be.With this practical guide, youll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Key players at Lightstep walk you through instrumenting your code for tracing, collecting the data that your instrumentation produces, and turning it into useful, operational insights. If you want to start implementing distributed tracing, this book tells you what you need to know.Youll learn:The pieces of a distributed tracing deployment: Instrumentation, data collection, and delivering valueBest practices for instrumentation (the methods for generating trace data from your service)How to deal with or avoid overhead, costs, and samplingHow to work with spans (the building blocks of request-based distributed traces) and choose span characteristics that lead to valuable tracesWhere distributed tracing is headed in the future

Give users the real-time experience they expect, by using Elixir and Phoenix Channels to build applications that instantly react to changes and reflect the application's true state. Learn how Elixir and Phoenix make it easy and enjoyable to create real-time applications that scale to a large number of users.

Early system administration required in-depth knowledge of a variety of services on individual systems. Now, the job is increasingly complex and different from one company to the next with an ever-growing list of technologies and third-party services to integrate. How does any one individual stay relevant in systems and services? This practical guide helps anyone in operationssysadmins, automation engineers, IT professionals, and site reliability engineersunderstand the essential concepts of the role today.Collaboration, automation, and the evolution of systems change the fundamentals of operations work. No matter where you are in your journey, this book provides you the information to craft your path to advancing essential system administration skills. Author Jennifer Davis provides examples of modern practices and tools with recommended materials to advance your skills.Topics include:Development and testing: Version control, fundamentals of virtualization and containers, testing, and architecture reviewDeploying and configuring services: Infrastructure management, networks, security, storage, serverless, and release managementScaling administration: Monitoring and observability, capacity planning, log management and analysis, and security and compliance