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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

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