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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

With this practical book, Kief Morris of ThoughtWorks shows you how to effectively use principles, practices, and patterns pioneered by infrastructure and development teams to manage cloud age infrastructure.

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

Carl Allchin from The Information Lab in London gets you up to speed on Tableau Prep through a series of practical lessons that include methods for preparing, cleaning, automating, organizing, and outputting your datasets.

Microservices architectures offer faster change speeds, better scalability, and cleaner, evolvable system designs. But implementing your first microservices architecture is difficult. How do you make myriad choices, educate your team on all the technical details, and navigate the organization to a successful execution to maximize your chance of success? With this book, authors Ronnie Mitra and Irakli Nadareishvili provide step-by-step guidance for building an effective microservices architecture.Architects and engineers will follow an implementation journey based on techniques and architectures that have proven to work for microservices systems. You'll build an operating model, a microservices design, an infrastructure foundation, and two working microservices, then put those pieces together as a single implementation. For anyone tasked with building microservices or a microservices architecture, this guide is invaluable.Learn an effective and explicit end-to-end microservices system designDefine teams, their responsibilities, and guidelines for working togetherUnderstand how to slice a big application into a collection of microservicesExamine how to isolate and embed data into corresponding microservicesBuild a simple yet powerful CI/CD pipeline for infrastructure changesWrite code for sample microservicesDeploy a working microservices application on Amazon Web Services

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

Cloud computing is typically associated with backend development and DevOps. But with the rise of serverless technologies and a new generation of services and frameworks, frontend and mobile developers can build robust applications with production-ready features such as authentication and authorization, API gateways, chatbots, augmented reality scenes, and more. This hands-on guide shows you how.Nader Dabit, developer advocate at Amazon Web Services, guides you through the process of building full stack applications using React, AWS, GraphQL, and AWS Amplify. Youll learn how to create and incorporate services into your client applications while learning general best practices, deployment strategies, rich media management, and continuous integration and delivery along the way.Learn how to build serverless applications that solve real problemsUnderstand what is (and isnt) possible when using these technologiesCreate a GraphQL API that interacts with DynamoDB and a NoSQL databaseExamine how authentication worksand learn the difference between authentication and authorizationGet an in-depth view of how serverless functions work and why theyre importantBuild full stack applications on AWS and create offline apps with Amplify DataStore

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.With this book, you'll learn how to build a modern production system in the cloud, viewed through the lens of serverless computing. You'll discover how serverless can free you from the tedious task of setting up and maintaining systems in production. You'll also explore new ways to level up your career and design, develop, and deploy with confidence.In three parts, this book includes:The Path to Production: Examine the ins and outs of distributed systems, microservices, interfaces, and serverless architecture and patternsThe Tools: Dive into monitoring, observability and alerting, logging, pipelines, automation, and deploymentConcepts: Learn how to design security and privacy, how to manage quality through testing and staging, and how to plan for failure

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

Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models.You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build.Get up to speed on the field of weak supervision, including ways to use it as part of the data science processUse Snorkel AI for weak supervision and data programmingGet code examples for using Snorkel to label text and image datasetsUse a weakly labeled dataset for text and image classificationLearn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

Get the authoritative guide to Dapr, the distributed application runtime that works with new and existing programming languages alike. Written by the models creators, this introduction shows you how Dapr not only unifies stateless, stateful, and actor programming models but also runs everywherein the cloud or on the edge.Authors Haishi Bai and Yaron Schneider, both with Microsofts Azure CTO team, explain that, with Dapr, you dont need to include any SDKs or libraries in your user code. Instead, you automatically get flexible binding, state management, the actor pattern, pub-sub, reliable messaging, and many more features. This book shows developers, architects, CIOs, students, and computing enthusiasts how to get started with Dapr.Learn the new programming model for cloud native applicationsWrite high-performance distributed applications without drilling into technical detailsUse Dapr with any language or framework to write microservices easilyLearn how Dapr provides consistency and portability through open APIs and extensible, community-driven componentsExplore how Dapr handles state, resource bindings, and pub-sub messaging to enable resilient event-driven architectures that scaleIntegrate cloud applications with various SaaS offerings, such as machine learning

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

As devices get smarter, they — and the companies that created them — gather more and more data about you, the user: what you like, where you go, even listening to and recording conversations around the house. It's easy to gloss over how much data is getting stored in "the cloud” everyday without you knowing, but with a little effort, you don't have to trade privacy for functionality.In this issue of Make:, we address this head-on. We'll show you how to set up your own DIY voice assistants using free and private tools that keep your data local while performing your favorite tasks. You can even run one on a Raspberry Pi! Then, configure a WebThings gateway from Mozilla to keep your IoT gadgets connected but private. And if you still can't give up Alexa's bells and whistles, at least consider assembling the Alias Privacy Parasite, which lets you decide when Alexa gets to listen in.The issue also continues our Fix the Planet series with a set of guidelines on effective ways to electrify your cars and homes to slash your carbon output (and save you money along the way).Plus, 17 exciting projects, including:Convert any bike to electric with a front wheel motor kitMake your needlework come alive with embroidery animationTips on converting a metal lathe to CNC with free softwareGrow vegan leather for projects and cosplayAnd much more!

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.

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