Search results for ""author manny"

Understanding Java from the JVM up gives you a solid foundation to grow your expertise and take on advanced techniques for performance, concurrency, containerization, and more. In The Well-Grounded Java Developer, Second Edition you will learn: The new Java module system and why you should use it Bytecode for the JVM, including operations and classloading Performance tuning the JVM Working with Java's built-in concurrency and expanded options Programming in Kotlin and Clojure on the JVM Maximizing the benefits from your build/CI tooling with Maven and Gradle Running the JVM in containers Planning for future JVM releases The Well-Grounded Java Developer, Second Edition introduces both the modern innovations and timeless fundamentals you need to know to become a Java master. Authors Ben Evans, Martijn Verburg, and Jason Clark distil their decades of experience as Java Champions, veteran developers, and key contributors to the Java ecosystem into this clear and practical guide. about the technology Java's history of innovation, its huge collection of libraries and frameworks, and the flexibility of the JVM have cemented its place as one of the world's most popular programming languages. Although it's easy to get started with Java, understanding how the language intersects with the JVM is the key to unlocking the power of this awesome language and its deep ecosystem of frameworks, tools, and alternative JVM-based languages. about the book The Well-Grounded Java Developer, Second Edition is a complete revision of the classic original with the latest innovations of the Java platform. It upgrades your existing Java skills with both JVM fundamentals like bytecode, and powerful new features such as modules and concurrency models. You'll broaden your understanding of what's possible by exploring Kotlin and other JVM languages, and learn how functional programming can offer a powerful new perspective. Each concept is illustrated with hands-on examples, including a fully modularized application/library, build setups for Maven and Gradle, and creating your own multithreaded application.

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You know how to build Go programs—now learn how to ship them to your customers efficiently! This practical guide to continuous delivery shows you how to rapidly establish an automated pipeline that will improve your testing, code quality, and final product. In Bootstrapping Go you will learn how to: Develop better software based on feedback from customers Create a development pipeline that turns feedback into features Reduce bugs with pipeline automation that validates code before it is deployed Establish continuous testing for exceptional code quality Serverless, container based, and server based deployments Scale your deployment in a cost-effective way Deliver a culture of continuous improvement Bootstrapping Go is a hands-on guide to shipping Go-based software. Following examples in the powerful Go programming language, you'll learn how to establish pipelines that seamlessly ferry your projects through production and deployment. Put the theory of continuous delivery and continuous integration into action, and discover instantly-useful guidance on automating your team's build and reacting with agility to customer demands. about the technology Development pipelines built to the principles of continuous delivery are the best way for code to flow through your organization. A properly functioning pipeline makes it seamless to modify functionality, enhance code quality, and evolve your deployments to meet your customer's needs. about the book Bootstrapping Go: Automating code development, testing and deployment shows you how to build pipelines that optimize your development process so you can deliver software seamlessly to production. You'll dive right in, learning author Joel Holmes's easy way to establish pipelines. In fact, you'll set up your first working pipeline before you're finished with Chapter three!

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DESCRIPTIONEvent Streams in Action is a foundational book introducing the ULPparadigm and presenting techniques to use it effectively in data-richenvironments. The book begins with an architectural overview,illustrating how ULP addresses the thorny issues associated withprocessing data from multiple sources. It then guides the readerthrough examples using the unified log technologies Apache Kafkaand Amazon Kinesis and a variety of stream processing frameworksand analytics databases. Readers learn to aggregate events frommultiple sources, store them in a unified log, and build data processingapplications on the resulting event streams. As readers progressthrough the book, they learn how to validate, filter, enrich, and storeevent streams, master key stream processing approaches, and exploreimportant patterns like the lambda architecture, stream aggregation,and event re-processing. The book also dives into the methods andtools usable for event modelling and event analytics, along withscaling, resiliency, and advanced stream patterns. KEY FEATURES • Building data-driven applications that are easier to design,deploy, and maintain• Uses real-world examples and techniques• Full of figures and diagrams• Hands-on code samples and walkthroughs This book assumes that the reader has written some Java code. SomeScala or Python experience is helpful but not required. ABOUT THE TECHNOLOGYUnified Log Processing is a coherent data processing architecture thatcombines batch and near-real time stream data, event logging andaggregation, and data processing into a unified event stream. By efficientlycreating a single log of events from multiple data sources, Unified LogProcessing makes it possible to design large-scale data-driven applicationsthat are easier to design, deploy, and maintain. AUTHOR BIOAlexander Dean is co-founder and technical lead of Snowplow Analytics,an open source event processing and analytics platform.

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"A great book to introduce developers to the essential role that logging plays in software design and support." - Trent Whiteley Logging in Action teaches you how to make your log processing a real asset for your application, all with free and open source tools. You'll use the powerful log management tool Fluentd to solve common log problems, and learn how proper log management can improve performance and make management of software solutions easier. Through useful examples like sending log driven events to Slack, you'll get hands-on experience applying structure to your unstructured data. about the technologyFluentd is a powerful log management tool that seamlessly handles messy logging data, from operational errors, to application events, and security events. It decouples log data, such as SNMP or slow database queries, from backend systems and easily sends it where it needs to go—thanks to 500+ flexible plugins covering all major services. about the bookLogging in Action is a guide to streamlining your log processing, turning logged data into a tool that will boost your application's performance. It's packed with tips and tricks on how proper log management can improve your application's security, speed, and cost-effectiveness. You'll master the basics of the Fluentd management tool, then take a peek behind the scenes at how different configurations will impact and improve the way your systems function. By the time you're done, you'll be able to reliably configure Fluentd so that it can easily scale to handle even the largest data loads. what's insideDeploy Fluentd and Fluent Bit into varied environmentsConfigure Fluentd and Fluent Bit to solve common log problemsFluentd for microservicesConnect a custom log source or destination with Fluentd's extensibility frameworkCreate a custom plugin for niche problemsLogging best practices and common pitfalls about the readerFor developers and operations people familiar with application logging or infrastructure monitor logs. about the authorPhil Wilkins has spent over 25 years in the software industry. He works as a Technology Evangelist for Capgemini, specializing in cloud integration, APIs, and related technologies. Phil is TOGAF certified, and recognized by Oracle as an Ace Director (independent technology advocate) for his contributions to the integration and PaaS community.

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Distributed across servers, difficult to test, and resistant to modification—modern software is complex. Grokking Simplicity is a friendly, practical guide that will change the way you approach software design and development. It introduces a unique approach to functional programming that explains why certain features of software are prone to complexity, and teaches you the functional techniques you can use to simplify these systems so that they’re easier to test and debug. Available in PDF (ePub, kindle, and liveBook formats coming soon). about the technologyEven experienced developers struggle with software systems that sprawl across distributed servers and APIs, are filled with redundant code, and are difficult to reliably test and modify. Adopting ways of thinking derived from functional programming can help you design and refactor your codebase in ways that reduce complexity, rather than encouraging it. Grokking Simplicity lays out how to use functional programming in a professional environment to write a codebase that’s easier to test and reuse, has fewer bugs, and is better at handling the asynchronous nature of distributed systems. about the bookIn Grokking Simplicity, you’ll learn techniques and, more importantly, a mindset that will help you tackle common problems that arise when software gets complex. Veteran functional programmer Eric Normand guides you to a crystal-clear understanding of why certain features of modern software are so prone to complexity and introduces you to the functional techniques you can use to simplify these systems so that they’re easier to read, test, and debug. Through hands-on examples, exercises, and numerous self-assessments, you’ll learn to organize your code for maximum reusability and internalize methods to keep unwanted complexity out of your codebase. Regardless of the language you’re using, the ways of thinking in this book will help recognize problematic code and tame even the most complex software. what's inside Apply functional programming principles to reduce codebase complexity Work with data transformation pipelines for code that’s easier to test and reuse Tools for modeling time to simplify asynchrony 60 exercises and 100 questions to test your knowledge about the readerFor experienced programmers. Examples are in JavaScript. about the author Eric Normand has been a functional programmer since 2001 and has been teaching functional programming online and in person since 2007. Visit LispCast.com to see more of his credentials.

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To create their own voice "skills," users need to learn some new device toolkits, the basics of Voice UI design, and some emerging best practices for building and deploying on these diverse platforms. Voice Applications for Alexa and Google Assistant guides readers in the exciting world of designing, building, and implementing voice-based applications for Amazon Alexa or Google Assistant! They learn how to build their own "skills"—the voice app term for actions the device can perform—from scratch. Key Features · Designing a voice interaction model · Fulfilling skills via a serverless platform like AWS Lambda · Connecting a skill to a database Audience Written for JavaScript developers interested in building voice-enabled applications. No prior experience required! Author Bio Dustin A. Coates is a web developer and web development instructor. He has taught hundreds of students online and offline at General Assembly. Dustin also developed popular courses for OneMonth.com and the European non-profit Konexio, which teaches refugees how to code.

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Data-Oriented Programming teaches you to design and implement software using the data-oriented programming paradigm. In it, you'll learn author Yehonathan Sharvit's unique approach to DOP that he has developed over a decade of experience.Every chapter contains a new light bulb moment that will change the way you think about programming. As you read, you'll build a library management system using the DOP paradigm. You'll design data models for business entities, manipulate immutable data collections, and write unit tests for data-oriented systems. About the TechnologyData-oriented programming is an exciting new paradigm that eliminates the usual complexity caused by combining data and code into objects and classes. In DOP, you maintain application data in persistent generic data structures separated from the program's code. You use general-purpose functions to manipulate the data without mutating it. This approach rids your applications of state-related bugs and makes your code much easier to understand and maintain.

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The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you’ve learned in previous chapters. By the end of the bookcamp, you’ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technologyMachine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that’s exactly what you’ll be doing in Machine Learning Bookcamp. about the bookIn Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you’re done working through these fun and informative projects, you’ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what's inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the readerFor readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.

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Modern software systems are composed of many servers, services, and other components that communicate through APIs. As a developer, your job is to make sure these APIs are stable, reliable, and easy to use for other developers. API Design Patterns provides you with a unique catalog of design standards and best practices to ensure your APIs are flexible and user-friendly. Fully illustrated with examples and relevant use-cases, this essential guide covers patterns for API fundamentals and real-world system designs, along with quite a few not-so-common scenarios and edge-cases. about the technologyAPI design patterns are a useful set of best practice specifications and common solutions to API design challenges. Using accepted design patterns creates a shared language amongst developers who create and consume APIs, which is especially critical given the explosion of mission-critical public-facing web APIs. API Patterns are still being developed and discovered. This collection, gathered and tested by Google API expert JJ Geewax, is the first of its kind. about the book API Design Patterns draws on the collected wisdom of the API community, including the internal developer knowledge base at Google, laying out an innovative set of design patterns for developing both internal and public-facing APIs. In this essential guide, Google Software Engineer JJ Geewax provides a unique and authoritative catalog of patterns that promote flexibility and ease-of-use in your APIs. Each pattern in the catalog is fully illustrated with its own example API, use-cases for solving common API design challenges, and scenarios for tricky edge issues using a pattern’s more subtle features. With the best practices laid out in this book, you can ensure your APIs are adaptive in the face of change and easy for your clients to incorporate into their projects. what's inside A full case-study of building an API and adding features The guiding principles that underpin most API patterns Fundamental patterns for resource layout and naming Advanced patterns for special interactions and data transformations about the readerAimed at software developers with experience using APIs, who want to start building their own. about the author JJ Geewax is a software engineer at Google, focusing on Google Cloud Platform and API design. He is also the author of Google Cloud Platform in Action.

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"Helps explain some of the knowledge gaps between enthusiastic new graduates and grouchy old gray beards like myself." - Joe Ivans Practical techniques for writing code that is robust, reliable, and easy for team members to understand and adapt. Good code or bad code? The difference often comes down to how you apply the conventions, style guides, and other established practices of the software development community. In Good Code, Bad Code you'll learn how to boost your effectiveness and productivity with code development insights normally only learned through years of experience, careful mentorship, and hundreds of code reviews. In Good Code, Bad Code you'll learn how to:- Think about code like an effective software engineer- Write functions that read like a well-structured sentence- Ensure code is reliable and bug free- Effectively unit test code- Identify code that can cause problems and improve it- Write code that is reusable and adaptable to new requirements- Improve your medium and long-term productivity- Save you and your team's time about the technologyCoding in a development team requires very different skills to working on personal projects. Successful software engineers need to ensure that their code is reusable, maintainable, and easy for others to understand and adapt. about the bookGood Code, Bad Code is a shortcut guide to writing high-quality code. Your mentor is Google veteran Tom Long, who lays out lessons and mindsets that will take your code from “junior developer” to “senior engineer.” This instantly-useful book distils the principles of professional coding into one comprehensive and hands-on beginner's guide. You'll start with a jargon-free primer to coding fundamentals that teaches you to think about abstractions, consider your fellow engineers, and write code that can recover from errors. Next, you'll dive into specific techniques and practices. You'll run through common coding practices to learn when to apply the right technique to your problem—and which might be best avoided! All practices are illustrated with annotated code samples written in an instantly recognizable pseudocode that you can relate to your favourite object-oriented language. By the time you're done, you'll be writing the kind of readable, reusable, and testable code that's the mark of a true software professional. about the readerFor coders looking to improve their experience in professional software development. about the authorTom Long is a software engineer at Google. He works as a tech lead, mentoring and teaching professional coding practices to new graduates and beginner software engineers.

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"If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. about the technologyDeep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars. about the bookInside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! about the readerFor Python programmers with basic machine learning skills. about the authorEdward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.

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A practical collection of the most important Python techniques. For beginning to intermediate Python programmers. Python How-To by Yong Cui, walks you through the most important coding techniques in Python. Whether you are doing data science, building web applications, or writing admin scripts, you will find answers to your “how-to” questions in this book. Author Yong Cui's clear and practical writing is perfect for beginners and veterans alike, with insightful code snippets, interesting graphics, and challenging exercises. Python How-To includes over 60 detailed answers to questions like: How do I join and split strings? How do I access dictionary keys, values, and items? How do I set and use the return value in function calls? How do I process JSON data? How do I create lazy attributes to improve performance? How do I change variables in a different namespace? ... and much more Along the way, you will also learn how to take advantage of Python's versatile tools and libraries. About the technology No matter where you are on your Python journey, you will run into situations where you ask yourself: “How do I do that?” This book provides concrete answers to the most common questions in areas such as manipulating text, working with collections, managing objects and functions, file management, etc.

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Description Why use several languages for a web application when you only need one? Node.js is a server-side platform and runtime that readers can use to build full stack web applications entirely in JavaScript! Get Programming with Node.js teaches readers to write server-side code in JavaScript using Node.js. In 34 fast-paced, fun, and practical lessons, readers discover how to extend their existing JavaScript skills to write back-end code for their web applications. Key features · Fast-paced · Practical lessons · Hands-on guide Audience Written for developers who know HTML, CSS, and JavaScript. No prior experience with Node.js is required. About the technology Node supports scalable, high-performance applications with easy asynchronous communication, an event-driven mindset, and a vast ecosystem of tools and modules. Node can handle the real-time response rates that games, chat sites, and statistical services need, and you get to program everything in JavaScript. Author biography Jonathan Wexler has an extensive background in computer theory and web development. Having curated a Node.js curriculum as the academic director and lead developer for The New York Code and Design Academy, Jonathan has instructed multiple intensive programs in full stack development and currently works as a senior developer for Bloomberg LP.

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There's a big gap between running machine learning and data processes as prototypes, and deploying them to a production cloud environment. Data Engineering on Azure reveals the architectural, operational, and data management techniques that power cloud-based data infrastructure built on the Microsoft Azure platform. Data Engineering on Azure teaches you to build a scalable and robust data platform to industry-leading standards. All examples are based on the production big data platform that powers Microsoft's customer growth operations. You'll learn techniques and best practices that author Vlad Riscutia and his team use on a daily basis, including automation and DevOps, running a reliable machine learning pipeline, and managing your data inventory. Examples are illustrated with Azure. The patterns and techniques are transferable to other cloud platforms. About the TechnologyBuild secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify.

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Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps. about the technologyNatural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines. about the book Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you’ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you’ll use in all different kinds of NLP programs. By the time you’re done, you’ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems. what's inside Design, develop, and deploy basic NLP applications NLP libraries such as AllenNLP and Fairseq Advanced NLP concepts such as attention and transfer learning about the readerAimed at intermediate Python programmers. No mathematical or machine learning knowledge required. about the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions.

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"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. about the technologyMachine learning has made remarkable progress in recent years. We've gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the bookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You'll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. what's insideDeep learning from first principlesImage-classification, imagine segmentation, and object detectionDeep learning for natural language processingTimeseries forecastingNeural style transfer, text generation, and image generation about the readerReaders need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. about the authorFrançois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

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DESCRIPTION Apache Camel is a Java library that lets users implement the standard enterprise integration patterns in a few lines of code. Camel in Action, Second Edition is the most complete Camel book on the market. It starts with core concepts like sending, receiving, routing, and transforming data and then shows readers the entire lifecycle. The book goes in depth on how to test, deal with errors, scale, deploy, and monitor apps and even how to build custom tooling. Written by core developers of Camel and the authors of the first edition, this book distills their experience and practical insights so that readers can tackle integration tasks like a pro. RETAIL SELLING POINTS • Uses the most up to date version of Camel. • New chapters on security, clustering, cloud integration, Docker, and Kubernetes. • Valuable examples in Java and XML. • Written by core developers of Camel. • Practical, insightful and comprehensive. AUDIENCE Readers should have basic Java skills. ABOUT THE TECHNOLOGY Camel is an integration framework that aims to make user’s integration projects productive and fun. One of the fundamental principles of Camel is that it makes no assumptions about the type of data you need to process. Camel’s focus is on simplifying integration.

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GraphQL radically improves the efficiency of web APIs, reducing the number of requests and the volume of data in-transit within your web applications. You can amplify the benefits of GraphQL by adjusting your stack to match GraphQL’s more natural representation of your data as a graph. In Fullstack GraphQL Applications with GRANDstack you’ll learn to build graph-aware end-to-end web applications using GraphQL, React, Apollo, and the Neo4j graph database. about the technologyOver-fetching or under-fetching data from REST APIs makes your applications slow and unreliable, especially over weak mobile network connections. The GraphQL query language offers a solution with a new API paradigm, reducing loads by constructing precise graph-based data requests. By adapting your application’s design and data store to leverage graphs throughout the stack, you decrease friction by using a consistent data model improving developer productivity, performance and maintainability. The combination of GraphQL, React, Apollo, and Neo4j Database, aka “the GRANDstack,” provides an easily adoptable end-to-end solution perfect for building fullstack GraphQL applications. about the book Fullstack GraphQL Applications with GRANDstack teaches you to leverage the power of GraphQL to create modern APIs that streamline data loads by allowing client applications to selectively fetch only the data required. GRANDstack.io contributor and GraphQL Foundation member William Lyon teaches you everything you need to know to design, deploy, and maintain a GraphQL API from scratch and create graph-aware fullstack web applications. In this project-driven book, you’ll build a complete business reviews application using the cutting-edge GRANDstack, learning how the different parts fit together. Chapter-by-chapter, you’ll master creating a GraphQL service with Apollo Server, modelling a GraphQL API with GraphQL type definitions, installing the Neo4j Database on different platforms, and more. By the time you’re done, you’ll be able to deploy all of the components of a serverless fullstack application in a secure and cost-effective way that takes full advantage of GraphQL’s performance capabilities. Along the way, you’ll also get tips for applying these techniques to other stacks. what's inside Building the backend functionality of a GraphQL application Handling authentication and authorization with GraphQL Implementing pagination and rate limiting in a GraphQL API Create a basic frontend application using React and Apollo Client about the readerAimed at developers comfortable building web applications using tools like Django, Rails, or MEAN stack and standard SQL-based databases. No experience with GraphQL or graph databases required. about the author William Lyon is a software developer at Neo4j, working on integrations with other technologies and helping users build applications with Neo4j. He is the creator and maintainer of neo4j-graphql.js, a JavaScript library for creating GraphQL APIs, and is a contributor to GRANDstack.io. He serves as Neo4j’s representative on the GraphQL Foundation.

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Automated testing will help you write high-quality software in less time, with more confidence, fewer bugs, and without constant manual oversight. Testing JavaScript Applications is a guide to building a comprehensive and reliable JS application testing suite, covering both how to write tests and how JS testing tools work under the hood. You’ll learn from Lucas de Costa, a core contributor to popular JS testing libraries, as he shares a quality mindset for making testing decisions that deliver a real contribution to your business. You’ll benefit from informative explanations and diagrams, easily-transferable code samples, and useful tips on using the latest and most consolidated libraries and frameworks of the JavaScript ecosystem. about the technologyNo developer wants to waste time making sure every application feature still works whenever they push new code to production. Thankfully, automated testing delivers quick and precise feedback on whether your application still functions correctly every time you update it. With automated testing, you can validate your application with a single command—and unlike humans, machines don’t forget steps or make mistakes! about the book Testing JavaScript Applications is a guide to creating JavaScript tests that are targeted to your application’s specific needs. Dripping with the insight author Lucas da Costa has developed as a core contributor to some of the most popular JS testing libraries, this book offers dozens of detailed code samples that you can apply to your own projects. You’ll learn how to write tests for both backend and frontend applications, covering the full spectrum of testing types so you can pick an approach that’s right for you. Taking on the role of a developer for a bakery’s web store, you’ll learn to validate different aspects including databases, third-party services, and how to spin-up a real browser instance to interact with the entire application. All examples are delivered using the popular testing tool Jest and modern packages of the JavaScript ecosystem. what's inside Writing practical tests that make a real business contribution Writing tests for both front-end and back-end applications Managing the costs and complexity of your tests Practicing test-driven development Dealing with external dependencies, like databases or third-party APIs Supporting tests by creating a "culture of quality" about the readerFor junior JavaScript developers. No testing experience required. about the author Lucas da Costa is a core maintainer of Chai and Sinon.JS, two of the most popular testing tools in the JavaScript ecosystem. He has also contributed to Jest and other relevant open-source projects. Lucas is committed to a culture of sharing and has spoken at major software engineering conferences, including JSConf Colombia, FluentConf, HolyJS, CityJSConf London, and many others.

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Serverless Architectures on AWS, Second Edition teaches you how to design, secure, and manage serverless backend APIs for web and mobile applications on the AWS platform. You'll get going quickly with this book's relevant real-world examples, code listings, diagrams, and clearly-described architectures that you can readily apply to your own work. You’ll master serverless systems using AWS Lambda and the myriad other services on the AWS platform. This new edition has been fully updated to reflect the newest serverless design best practices and changes to AWS. It features two entirely new chapters dedicated to DevOps, monitoring, and microservices, as well as working with DynamoDB, GraphQL and Kinesis. Key Features · First steps with serverless computing · The principles of serverless design · Transitioning from servers to services · Writing AWS Lambda functions and using the API Gateway Audience This book is for server-side and full-stack software developers with some experience using Node.js and AWS. Author Bio Peter Sbarski is VP of Engineering at A Cloud Guru and the head organizer of Serverlessconf, the world’s first conference dedicated entirely to serverless architectures and technologies. Throughout his extensive IT career, Peter has led teams across large enterprise solutions with a focus on web and AWS cloud technologies.

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"Not only for MacOS and Linux users, but also a great resource for Windows PS users." - Bruce Bergman Learn PowerShell in a Month of Lunches: Covers Windows, Linux, and macOS is a task-focused tutorial for administering Linux and macOS systems using Microsoft PowerShell. Adapted by PowerShell team members Travis Plunk and Tyler Leonhardt from the bestselling Learn Windows PowerShell in a Month of Lunches by community legends Don Jones and Jeffrey Hicks, it features Linux-based examples covering core language features and admin tasks. Designed for busy IT professionals, this innovative guide will take you from the basics to PowerShell proficiency through 25 tutorials you can do in your lunch break. about the technologyThe PowerShell scripting language and administrative shell was initially created for Windows, providing a high-quality command-line interface and awesome automation features. As part of Microsoft's ongoing strategy to support non-Windows platforms with its Azure cloud service and .NET Core framework, PowerShell now runs on Linux and macOS. Like Bash, PowerShell can execute and script nearly any aspect of Linux, so you can easily manage repetitive daily tasks, servers, Cloud resources, Continuous Integration pipelines, and more. Because PowerShell is a full-featured programming language, however, it provides capability well beyond traditional shell scripting languages, such as the ability to treat OS components as objects. about the bookLearn PowerShell in a Month of Lunches: Covers Windows, Linux, and macOS is a user-friendly tutorial to managing Linux and macOS systems with PowerShell. It's based on the bestselling Learn Windows PowerShell in a Month of Lunches, which has introduced PowerShell to nearly 100,000 readers. You'll learn how PowerShell shapes up to Bash or Python scripting as you write and run simple scripts that automate boring daily tasks. As you progress through the book, you'll use PowerShell to write Continuous Integration Pipelines and manage cloud-based servers. Just set aside one hour a day for a month, and you'll be automating tasks faster than you ever thought possible! what's inside- Why you should use PowerShell on Linux and macOS- Background jobs and automation techniques- Simple scripting to automate repetitive daily tasks- Common syntax and commands cheat sheet- Each lesson takes you an hour or less about the readerFor IT professionals comfortable administering Windows or Linux. No previous experience with PowerShell or Bash required. about the authorTravis Plunk has been a Software Engineer on various PowerShell teams since 2013, and at Microsoft since 1999. He was involved in open sourcing PowerShell and has worked on the project full time since shortly after the project was announced. James Petty is a Microsoft MVP, and the CEO and Executive Director for the DevOps Collective and PowerShell.org. Tyler Leonhardt has been a Software Engineer on the PowerShell team since 2017, and at Microsoft since 2016. He is a core maintainer of the PowerShell extension for Visual Studio Code. Learn Windows PowerShell in a Month of Lunches was written by PowerShell community legends Don Jones and Jeffrey Hicks, who have years of experience as successful PowerShell trainers.

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From its humble beginnings a container orchestration system, Kubernetes has become the de facto infrastructure for cloud native applications. Kubernetes impacts every aspect of the application development lifecycle, from design through deployment. To build and operate reliable cloud native systems, you need to understand what's going on below the surface. Core Kubernetes is packed with experience-driven insights and practical techniques, and takes you inside Kubernetes to teach you what you'll need to know to keep your system running like a well-oiled machine and prevent those panicked 3 AM phone calls. about the technologyKubernetes is a platform-independent layer for deploying scalable container-based applications. Kubernetes provides abstractions for clustering, replicating, and scaling system components in real time. It's especially well-suited for microservices applications, where you can easily balance the load among services. Kubernetes has become a core component of cloud native applications, and all major cloud vendors now offer hosted versions of Kubernetes. Kubernetes was initially developed by Google and is now governed as an open source project by the Cloud Native Computing Foundation (CNCF). about the bookCore Kubernetes is a reference guide designed to teach operators, SREs, and developers how to improve reliability and performance of Kubernetes-based systems. In it, Kubernetes experts Chris Love and Jay Vyas provide a guided tour through all major aspects of Kubernetes, from managing iptables to setting up dynamically scaled clusters that respond to changes in load. You'll understand the unique security concerns of container-based applications, discover tips to minimize costly unused capacity, and get pro tips for maximizing performance. This awesome collection of undocumented internals, expert techniques, and practical guidance has invaluable information you won't find anywhere else. what's inside- Kubernetes base components- Kubernetes networking- Storage and the Container Storage Interface- External load balancing and ingress- Kubernetes security- Different ways of creating a Kubernetes cluster- Configuring Kubernetes to use a GPU about the readerWritten for readers with a working knowledge of Kubernetes and microservices applications. about the authorAfter earning his PhD, Jay Vyas started his career contributing to Kubernetes' core features at RedHat, and next, he focused on helping multiple companies migrate to Cloud-Native solutions. He now resides at VMWare, once again contributing to Kubernetes and associated projects. When not coding, Jay likes to play with his video games, his synthesizers, and hang out in his Pod. As a thought leader in the Kubernetes and DevOps segment, Chris Love is a founder of multiple technology companies with over 20 years' experience in software engineering. A Google Cloud Certified Fellow, Chris has contributed to several open-source projects including Kubernetes, Bazel, and Terraform. He enjoys speaking around the world about DevOps and Kubernetes and practices martial arts in his free time.

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Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML system’s infrastructure. Following a real-world use case for calculating taxi fares, you’ll learn how to get a serverless ML pipeline up and running using AWS services. Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware. about the technologyYour new machine learning model is ready to put into production, and suddenly all your time is taken up by setting up your server infrastructure. Serverless machine learning offers a productivity-boosting alternative. It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems. With the serverless capabilities of major cloud vendors handling your infrastructure, you’re free to focus on tuning and improving your models. about the book Cloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You’ll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you’ll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you’ll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When you’re done, you’ll have the tools to easily bridge the gap between ML models and a fully functioning production system. what's inside Extracting, transforming, and loading datasets Querying datasets with SQL Understanding automatic differentiation in PyTorch Deploying trained models and pipelines as a service endpoint Monitoring and managing your pipeline’s life cycle Measuring performance improvements about the readerFor data professionals with intermediate Python skills and basic familiarity with machine learning. No cloud experience required. about the author Carl Osipov has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the world’s foremost experts in machine learning and also helped manage the company’s efforts to democratize artificial intelligence. You can learn more about Carl from his blog Clouds With Carl.

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Learn data science with Python by building five real-world projects! In Data Science Bookcamp you’ll test and build your knowledge of Python and learn to handle the kind of open-ended problems that professional data scientists work on daily. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. about the technologyIn real-world practice, data scientists create innovative solutions to novel open ended problems. Easy to learn and use, the Python language has become the de facto language for data science amongst researchers, developers, and business users. But knowing a few basic algorithms is not enough to tackle a vague and thorny problem. It takes relentless practice at cracking difficult data tasks to achieve mastery in the field. That’s just what this book delivers. about the book Data Science Bookcamp is a comprehensive set of challenging projects carefully designed to grow your data science skills from novice to master. Veteran data scientist Leonard Apeltsin sets five increasingly difficult exercises that test your abilities against the kind of problems you’d encounter in the real world. As you solve each challenge, you’ll acquire and expand the data science and Python skills you’ll use as a professional data scientist. Ranging from text processing to machine learning, each project comes complete with a unique downloadable data set and a fully-explained step-by-step solution. Because these projects come from Dr. Apeltsin’s vast experience, each solution highlights the most likely failure points along with practical advice for getting past unexpected pitfalls. When you wrap up these five awesome exercises, you’ll have a diverse relevant skill set that’s transferable to working in industry. what's inside Five in-depth Python exercises with full downloadable data sets Web scraping for text and images Organise datasets with clustering algorithms Visualize complex multi-variable datasets Train a decision tree machine learning algorithm about the readerFor readers who know the basics of Python. No prior data science or machine learning skills required. about the author Leonard Apeltsin is a senior data scientist and engineering lead at Primer AI, a startup that specializes in using advanced Natural Language Processing techniques to extract insight from terabytes of unstructured text data. His PhD research focused on bioinformatics that required analyzing millions of sequenced DNA patterns to uncover genetic links in deadly diseases.

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"I am in awe of this book, it is a very good read and has tons of interesting lessons for any engineer." - Tim van Deurzen Your brain responds in a predictable way when it encounters new or difficult tasks. This unique book teaches you concrete techniques rooted in cognitive science that will improve the way you learn and think about code. In The Programmer's Brain: What every programmer needs to know about cognition you will learn: - Fast and effective ways to master new programming languages- Speed reading skills to quickly comprehend new code- Techniques to unravel the meaning of complex code- Ways to learn new syntax and keep it memorized- Writing code that is easy for others to read- Picking the right names for your variables- Making your codebase more understandable to newcomers- Onboarding new developers to your team Learn how to optimize your brain's natural cognitive processes to read code more easily, write code faster, and pick up new languages in much less time. This book will help you through the confusion you feel when faced with strange and complex code, and explain a codebase in ways that can make a new team member productive in days! about the technologyUnderstanding the cognitive functions that govern the way your brain thinks about coding will help you work smarter, not harder. You'll improve your productivity, reduce your need for constant rewrites, and say goodbye to spending late nights struggling with new languages. about the bookThe Programmer's Brain explores the way your brain works when it's thinking about code. In it, you'll master practical ways to apply these cognitive principles to your daily programming life. You'll improve your code comprehension by turning confusion into a learning tool, and pick up awesome techniques for reading code and quickly memorizing syntax. This practical guide includes tips for creating your own flashcards and study resources that can be applied to any new language you want to master. By the time you're done, you'll not only be better at teaching yourself—you'll be an expert at bringing new colleagues and junior programmers up to speed. about the readerFor programmers who have experience working in more than one language. about the authorDr. Felienne Hermans is an associate professor at Leiden University in the Netherlands. She has spent the last decade researching learning and teaching programming. Felienne is an award-winning educator, the creator of the Hedy programming language for novice programmers, and a host of Software Engineering Radio—one of the world's largest programming podcasts.

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The “servicemesh” pattern, implemented by platforms like Istio, helps you push operational issues into the infrastructure so the application code is easier to understand, maintain, and adapt. Istio in Action teaches you how to implement a full-featured Istio-based service mesh to manage a microservices application. Istio in Action is a comprehensive guide to handling authentication, routing, retrying, load balancing, collecting data, security, and other common network-related tasks using the Istio service mesh platform. With helpful diagrams and hands-on examples, you'll learn how to use this open-source service mesh to control routing, secure container applications, and monitor network traffic. You will also bring Istio to legacy systems without changes to your applications and discover how to use Istio in amulti-cloud world with the data layer deployed on a cluster like Kubernetes. Cloud-native applications can include thousands of clustered containers, distributed components, and complex interactions. To build them effectively, developers need a new approach to infrastructural concerns like monitoring, storage, scaling, orchestration, and security. The Istio platform offers a configurable infrastructure layer called a service mesh that reliably and efficiently manages day-to-day concerns like service discovery, load balancing, encryption, authentication and authorization, circuit breakers, and more. Open source andcloud-ready, Istio is a welcome upgrade from manually managed microservices infrastructure.

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Solve common application design and usability issues with flair! Design for Developers introduces the essential design and UX techniques to help you create good user experiences, iterate smoothly on front-end features, and collaborate effectively with designer colleagues. Author Stephanie Stimac shares the unique insights she's learned as a designer for the Microsoft Developer Experiences team. This one-of-a-kind book provides a developer-centric approach to the essential design fundamentals of modern web applications. Key features include: Use colour, typography, and layout to create a hierarchy on a web page Apply colour palettes consistently in a user interface Choose the correct typefaces and fonts Conduct user research to validate design decisions Quickly plan a website's layout and structure Ideal for web developers familiar with HTML, CSS, and JavaScript basics, you will learn how to craft a polished visual design with just colour, space, and typeface and put all your new skills into practice to design a website from scratch. About the technology Having good design sense will make you a better web developer. This practical guide teaches you essential design and UX principles so you can speak the language of design and make more informed decisions about how you build your apps. You will also get up to speed with the design workflow and learn how to integrate the design process effectively into the development lifecycle.

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Reading and storing data is a core part of any application, and .NET developers want database access to be easy and intuitive. Entity framework Core is a .NET library designed to simplify data persistence, bridging the mismatch between the different structures of object-oriented code and relational databases. Entity Framework Core in Action teaches developers how to add database functionality to .NET applications with EF Core. Key features · Clear Introduction · Teaches from real-world applications · Hands-on examples Audience This book assumes readers are familiar with .NET development and some understanding of what relational databases are. No experience with SQL needed. About the technology With EF Core, you can access data using abstract objects and properties without tightly coupling your code to the underlying relational database structure. And because it's part of Microsoft's open source .NET Core initiative, EF works on Windows, Linux and MacOS, and even mobile platforms via Microsoft's Xamarin. Author biography Jon Smith is a full-stack software developer and architect who focuses on Microsoft's ASP.NET web applications using Entity Framework (EF) ORM on the server-side, with various front-end JavaScript libraries. Jon is especially interested in defining patterns and building libraries that improve the speed of development of ASP.NET web/database applications.

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Own Your Tech Career: Soft skills for technologists is a guide to taking control of your professional life. It teaches you to approach your career with planning and purpose, always making active decisions towards your goals. Whatever your road to success, you’ll benefit from the toolbox of career-boosting techniques you’ll find in Own Your Tech Career: Soft skills for technologists. You’ll discover in-demand communication and teamwork skills, essential rules for professionalism, tactics of the modern job hunt, and more. In Own Your Tech Career: Soft skills for technologists, you will: Define what “success” means for your career Discover personal branding and career maintenance Prepare for and conduct a tech job hunt Spot speed bumps and barriers that can derail your progress Learn how to navigate the rules of the business world Perform market analysis to keep your tech skills fresh and relevant about the technologySuccess in a technology career requires more than technical ability. Achieving your career goals takes clear communication, top-notch time and productivity management, and a knack for navigating business needs. These “soft skills” are vital for a well-rounded career in tech, whether as a valuable employee or a successful freelancer. about the book Own Your Tech Career: Soft skills for technologists is a guide to getting what you want out of your career in tech. You’ll start by defining your ambitions—whether that’s a high salary, a great work/life balance, or something else entirely. Once you know where you’re going, you’ll begin your journey to success with the adaptable advice laid out in this book to help you on your way. You’ll take a deep dive into the communication, conflict resolution, and teamwork skills that employers prize in technologists, and master “nine rules of professionalism” that will keep you on track through the daily grind. Finally, you’ll pay it forward with advice on becoming an amazing mentor of your juniors, and leader of your peers. about the readerFor professionals who want to take control of their career in technology. about the authorMicrosoft MVP Don Jones brings his years of experience as a successful IT trainer to this engaging guide. Don is a sought-after conference speaker, career mentor, and author of the international bestseller Learn Windows PowerShell in a Month of Lunches. He has led dozens of career and job workshops for tech professionals.

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"An absolutely wonderful book for someone that has tried and failed to understand functional programming." - William E. Wheeler Grokking Functional Programming is a practical book written especially for object-oriented programmers. It will help you map familiar ideas like objects and composition to FP concepts such as programming with immutable data and higher-order functions. You will learn how to write concurrent programs, how to handle errors and how to design your solutions with modularity and readability in mind. And you'll be pleased to know that we skip the academic baggage of lambda calculus, category theory, and the mathematical foundations of FP in favor of applying functional programming to everyday programming tasks. At the end of the book, you'll be ready to pick a functional language and start writing useful and maintainable software. about the technologyFunctional programming is more than just writing the same old code in Scala, Clojure, or Haskell. To grok FP—to really get it—you need to rewire your brain to see the world differently. We're here to help you flip the switch. Grokking Functional Programming teaches you first to break down problems in a new way so you can approach them from a FP mindset. Following carefully-selected examples with thorough, carefully-paced explanations, you'll immerse yourself in FP concept by concept. Along the way, exercises, checks for understanding, and even the occasional puzzler give you opportunities to think and practice what you're learning. what's insideDesigning with functions and types instead of objectsMultiple learning approaches to help you grok each new conceptA practical programming-first teaching styleProgramming with pure functions & immutable valuesWriting concurrent programs with a functional styleTesting functional programs about the readerThe book assumes that the reader has at least one year of experience developing software using a mainstream object-oriented programming language like Java. While examples use Scala, this is not a Scala book. The concepts will apply to any FP language, and no prior knowledge of Scala or FP is required. about the authorMichał Płachta started using Scala commercially in 2014 and has been an active contributor to the Scala and JVM communities since. He regularly speaks at conferences, runs workshops, and organizes meetups to help others become better at functional programming. You can find his blog at michalplachta.com.

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F# leads to quicker development time and a lower total cost of ownership. Its powerful feature set allows developers to more succinctly express their intent, and encourages best practices - leading to higher quality deliverables in less time. Programming with F#: A guide for .NET developers shows you how to upgrade your .NET development skills by adding a touch of functional programming in F#. In just 43 bite-size chunks, you’ll learn to use F# to tackle the most common .NET programming tasks. You’ll start with the basics of F# and functional programming, building on your existing skills in the .NET framework. Examples use the familiar Visual Studio environment, so you’ll be instantly comfortable. Packed with enlightening examples, real-world use cases, and plenty of easyto-digest code, this easy-to-follow tutorial will make you wonder why you didn’t pick up F# years ago! KEY FEATURES • Hands-on chapters • Practical examples • Bite-size lessons • Try This exercises For intermediate C# and Visual Basic .NET developers who have heard about F# and functional programming and want to understand the benefits and use it as a part of their existing toolbox without having to throw away existing code. ABOUT THE TECHNOLOGY F# is a mature, open-source functional-first language that's rapidly growing in popularity both inside and outside the .NET ecosystem. AUTHOR BIO Isaac Abraham is an F# MVP and a .NET developer since .NET 1.0 with an interest in cloud computing and distributed data problems. He lives in both the UK and Germany, and is the director of Compositional IT.

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By dividing large applications into separate self-contained units, Microservices are a great step toward reducing complexity and increasing flexibility. Spring Microservices in Action, Second Edition teaches you how to build microservice-based applications using Java and the Spring platform. This second edition is fully updated for the latest version of Spring, with expanded coverage of API routing with Spring Cloud Gateway, logging with the ELK stack, metrics with Prometheus and Grafana, security with the Hashicorp Vault, and modern deployment practices with Kubernetes and Istio. about the technologyMicroservices break up your code into independent interconnected services that require careful forethought and design. Fortunately, Spring Boot, Spring Cloud, and Spring Cloud Gateway simplify the tedious plumbing and infrastructure setup required for microservice applications. Spring Boot removes the boilerplate code involved with writing a REST-based service. Spring Cloud provides a suite of tools for the discovery, routing, and deployment of microservices to the enterprise and the cloud. Spring Cloud Gateway provides a clear and effective routing to APIs, with a single entry point into a system. Together, they make it a snap to spin up Spring applications as microservices and wire them together into a reliable, scalable system. about the bookFully updated and upgraded for the latest version of Spring, Spring Microservices in Action, Second Edition is an expanded revision of a Manning bestseller. In it, you’ll learn how to build microservice-based applications using Java and the Spring platform and how to efficiently monitor and log your microservices operations. Throughout the book, carefully selected real-life examples expose microservice-based patterns for configuring, routing, scaling, and deploying your services. You'll see how Spring's intuitive tooling can help augment and refactor existing applications with microservices and how Spring Cloud Gateway makes it easy to handle multiple APIs with a single tool. You’ll also pick up best practices for using Spring with modern deployment platforms based on Kubernetes and Istio. what's inside Core microservice design principles Microservices best practices Using docker containers to run microservices Managing configuration with Spring Cloud Config and Hashicorp Vault for sensitive information Client-side resiliency with Hystrix, and Ribbon Managing application metrics with Prometheus and Grafana Intelligent routing using Spring Cloud Gateway Distributed tracing with Spring Cloud Sleuth, Zipkin and ELK Stack Deploying Spring Cloud applications with Kubernetes and Istio about the readerThis book is written for developers with Java and Spring experience. about the authors John Carnell is a senior cloud engineer with twenty years of experience in Java. Illary Huaylupo Sánchez is a software engineer with an MBA in IT management and over twelve years of experience in Java.

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Scala is a multi-style programming language for the JVM that supports both object-oriented and functional programming. Master Scala, and you'll be well-equipped to match your programming approach to the type of problem you're dealing with. Packed with examples and exercises, Get Programming with Scala is perfect starting point for developers with some OO knowledge who want to learn Scala and pick up a few FP skills along the way. about the technologyAlthough it’s a relative newcomer, Scala turned out to be the right language at the right time, opening up the world of functional programming to the JVM. Scala looks a lot like Java, and it interacts seamlessly with familiar Java libraries and tools. Since it fully supports functional programming, Scala gives you the freedom to choose the right programming style exactly when it benefits you. Scala can be daunting at first, especially if you’re seeing FP ideas for the first time. Fortunately, with the examples and exercises in this book, you’ll get over the initial learning hump quickly and start doing interesting projects before you know it! about the book Get Programming with Scala teaches you the core skills you'll need to code with Scala. This book lives up to its name! You'll learn the ins and outs of Scala by actually programming with it, thanks to the practical examples, exercises, and easy-to-follow lessons. You'll start by reviewing OOP concepts in the Scala language. Then, example-by-example, you'll gradually open up the world of functional programming. You'll explore functions and types and learn how to combine them to create powerful, flexible abstractions. Along the way, you'll learn some Scala testing and get an intuition for sniffing out and fixing code that doesn't smell quite right. what's inside Learn to express OO principles in Scala Express your program designs in functions Make use of types to enforce program requirements Use abstractions to avoid code duplication Write meaningful tests and recognize code smells about the readerWritten for developers with some prior experience in an OOP language like Java or C#. No experience with Scala or functional programming is needed. about the author Daniela Sfregola started working with Scala in 2013. Since then she has been an active contributor to the Scala Community. Daniela currently maintains several open source projects in the community and regularly speaks at Scala conferences. She blogs about Scala regularly at https://danielasfregola.com

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Distributed applications demand reliable, high-performance messaging. The Apache Pulsar server-to-server messaging system provides a secure, stable platform without the need for a stream processing engine like Spark. Contributed by Yahoo to the Apache Foundation, Pulsar is mature and battle-tested, handling millions of messages per second for over three years at Yahoo. Apache Pulsar in Action is a comprehensive and practical guide to building high-traffic applications with Pulsar, delivering extreme levels of speed and durability. about the technology Pulsar is a streaming messaging system designed for high performance server-to-server messaging. Built and tested under intense conditions at Yahoo, Pulsar has been proven in production and can handle millions of messages per second. Now free and open-source, Pulsar’s unique architecture helps solve some of the challenges of modern development. Pulsar avoids latency in streaming data transmission, making it a powerful tool for IoT Edge analytics. Its unified messaging model improves the performance of microservices architecture, and its tiered storage capabilities allow for larger volumes of data to be handled without fear of data loss. Pulsar’s flexible API interface works with Java, C++, Python, and Go, making it easy to incorporate Pulsar into your stack. about the book Apache Pulsar in Action is a hands-on guide to building scalable streaming messaging systems for distributed applications and microservices systems. You’ll start with Pulsar’s fundamentals, each illustrated by real-world examples, as you get to grips with Pulsar’s unique architecture. Pulsar contributor David Kjerrumgaard teaches the skills you need to deploy a Pulsar server, ingest data from third-party systems, and deploy lightweight computing logic with simple functions. You’ll learn to employ Pulsar’s seamless scalability through relatable case studies, including an IOT analytics application that can be deployed within a resource constrained environment and a microservices application based on Pulsar functions. At the end of this practical book, you’ll be ready to fully take advantage of Pulsar to create high-traffic message-driven applications. what's inside Publish from Apache Pulsar into third-party data repositories and platforms Design and develop Apache Pulsar functions Perform interactive SQL queries against data stored in Apache Pulsar Examples of Pulsar-based microservices that you can download and try yourself about the reader Written for experienced Java developers. No prior knowledge of Pulsar is needed. about the author David Kjerrumgaard is the Director of Solution Architecture at Streamlio, and a contributor to the Apache Pulsar and Apache NiFi projects.

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"A must-read for engineers and solution architects who work with streams on a regular basis." - Damian Esteban Every action by a user or a system process generates valuable data for your application or organization. Streaming systems capture and process these events, turning disconnected bits into coherent, useful information sources. Grokking Streaming Systems is a simple guide to the complex concepts you need to start building your own streaming systems. In this friendly, framework-agnostic tutorial, you'll learn how to handle real-time events and how to design and implement a system that's a perfect fit for your needs. Each new idea is carefully explained with diagrams, clear examples, and fun dialogue between perplexed personalities! about the technologyMany modern organizations rely on real-time event data to ensure top performance. In its raw state, most event data is unfiltered and hard to analyze. Streaming systems address this problem by processing event data so it can be used to create alerts, analysis dashboards, and automated responses, and to trigger other actions within a system. From live financial information to monitoring for signs of a DDoS attack and blocking rogue agents, streaming systems provide a big boost to the health, security, and flexibility of your applications. about the bookGrokking Streaming Systems helps you unravel what streaming systems are, how they work, and whether they're right for your business. Written to be tool-agnostic, you'll be able to apply what you learn no matter which framework you choose. You'll start with the key concepts and then work your way through increasingly complex examples, including tracking a real-time count of IoT sensor events and detecting fraudulent credit card transactions in real-time. You'll even be able to easily experiment with your own streaming system, by downloading the custom-built and super-simplified streaming framework designed for this book. By the time you're done, you'll be able to easily assess the capabilities of streaming frameworks, and solve common challenges that arise when building streaming systems. what's inside- Implement and troubleshoot streaming systems- Design streaming systems for complex functionalities- Assess parallelization requirements- Spot networking bottlenecks and resolve back pressures- Group data for high-performance systems- Handle delayed events in real-time systems about the readerFor readers interested in data processing. Examples in Java. about the authorJosh Fischer and Ning Wang are both Apache Committers, and part of the project management committee for the Apache Heron distributed stream processing engine. Josh is a software engineer at Scotcro and has worked with moving large datasets in real time for organizations such as 1904labs and Bayer. Ning is a software engineer at Amplitude building real-time data pipelines. He was a key contributor of Apache Heron in Twitter's Real-time Compute team.

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Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you’ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You’ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technologyAccess to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization's data, and present it as useful business insights. about the bookIn Designing Cloud Data Platforms, you’ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you’ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you’ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what's inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the readerFor data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years.

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Deep learning has revealed ways to create algorithms for applications that we never dreamed were possible. For software developers, the challenge lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Design Patterns is here to help. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Written by Google deep learning expert Andrew Ferlitsch, it's filled with the latest deep learning insights and best practices from his work with Google Cloud AI. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. about the technologyYou don't need to design your deep learning applications from scratch! By viewing cutting-edge deep learning models as design patterns, developers can speed up their creation of AI models and improve model understandability for both themselves and other users. about the book Deep Learning Design Patterns distills models from the latest research papers into practical design patterns applicable to enterprise AI projects. Using diagrams, code samples, and easy-to-understand language, Google Cloud AI expert Andrew Ferlitsch shares insights from state-of-the-art neural networks. You'll learn how to integrate design patterns into deep learning systems from some amazing examples, including a real-estate program that can evaluate house prices just from uploaded photos and a speaking AI capable of delivering live sports broadcasting. Building on your existing deep learning knowledge, you'll quickly learn to incorporate the very latest models and techniques into your apps as idiomatic, composable, and reusable design patterns. what's inside Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Composable design pattern for automatic learning methods Assembling large-scale model deployments Complete code samples and example notebooks Accompanying YouTube videos about the readerFor machine learning engineers familiar with Python and deep learning. about the author Andrew Ferlitsch is an expert on computer vision and deep learning at Google Cloud AI Developer Relations. He was formerly a principal research scientist for 20 years at Sharp Corporation of Japan, where he amassed 115 US patents and worked on emerging technologies in telepresence, augmented reality, digital signage, and autonomous vehicles. In his present role, he reaches out to developer communities, corporations and universities, teaching deep learning and evangelizing Google's AI technologies.

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"Do you want to learn more about software telemetry? Don't look any further, this book is the one you need." - Sander Zegveld Software telemetry is the discipline of tracing, logging, and monitoring infrastructure by observing and analyzing the events generated by the system. In Software Telemetry, you'll master the best practices for operating and updating telemetry systems. This practical guide is filled with techniques you can apply to any organization upgrading and optimizing their telemetry systems, from lean startups to well-established companies. You'll learn troubleshooting techniques to deal with every eventuality, such as building easily-auditable systems, preventing and handling accidental data leaks, and ensuring compliance with standards like GDPR. about the technologyComplex systems can become black boxes. Telemetry provides feedback on what's happening inside. Telemetry systems are built for gathering, transforming, and communicating data on the performance, functionality, processing speeds, errors, and security events of production systems. There are many forms of telemetry systems, from classic centralized logging to cutting-edge distributed tracing that follows data across microservices. But despite their difference in functionality, all telemetry systems share core operational similarities—and best practices for optimizing them to support your business needs. about the bookSoftware Telemetry is a guide to operating the telemetry systems that monitor and report on your applications. It takes a big picture view of telemetry, teaching you to manage your logging, metrics, and events as a complete end-to-end ecosystem. You'll learn the base architecture that underpins any software telemetry system, allowing you to easily integrate new systems into your existing infrastructure, and how these systems work under the hood. Throughout, you'll follow three very different companies to see how telemetry techniques impact a software-producing startup, a large legacy enterprise, and any organization that writes software for internal use. You'll even cover how software telemetry is used by court processes—ensuring that when your first telemetry discovery request arrives, there's no reason to panic! what's inside- Processes for legal compliance- Cleaning up after toxic data spills and leaks- Safely handling toxic telemetry and confidential records- Multi-tenant techniques and transformation processes- Updating metrics aggregation and sampling traces to display accurate data for longer- Revising software telemetry emissions to be easier to parse- Justifying increased spend on telemetry software about the readerFor software developers and infrastructure engineers supporting and building telemetry systems. about the authorJamie Riedesel is a staff engineer at Dropbox. She has over twenty years of experience in IT, working in government, education, legacy companies, and startups. She has specialized in DevOps for the past decade, running distributed systems in public clouds, getting over workplace trauma, and designing software telemetry architectures.

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"This is one of the best technical books I've ever read. The writing is so good, and it covers an incredible amount of knowledge. Hands down, this is the best reference material on using asyncio anywhere." - Kent R. Spillner Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library. Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio teaches you how to boost Python's performance by applying a variety of concurrency techniques. You'll learn how the complex-but-powerful asyncio library can achieve concurrency with just a single thread and use asyncio's APIs to run multiple web requests and database queries simultaneously. The book covers using asyncio with the entire Python concurrency landscape, including multiprocessing and multithreading. about the technologyThe time demands of running code synchronously quickly overload standard Python and slow your programs to a crawl. Python's Asynchronous I/O library asyncio was built to solve these performance problems by making it easy to divide and schedule computational tasks so they can be run independently. asyncio concurrently handles multiple operations without a drop in throughput or responsiveness, making your apps lightning fast and easier to scale. about the bookPython Concurrency with asyncio teaches you to write concurrent Python code that will boost the speed of your apps and APIs. The book demystifies asynchio's unique single-threaded concurrency model, giving you a behind-the-scenes understanding of the library and its new async/await syntax. Hard-to-grok concurrency topics are broken down into simple flowcharts so you can easily see how your coroutines and tasks are running. You'll learn to apply asyncio to solve common performance problems, such as batch database jobs, slow web servers, and scaling microservices. All examples you'll build are designed to be usable in the real world, including a clever command line SQL client that can run multiple slow queries at the same time. By the time you're done, you'll even be able to combine asyncio with traditional multiprocessing and multithreading techniques for huge improvements to performance. what's insideUse coroutines and tasks alongside async/await syntax to run code concurrentlyBuild web APIs and make concurrency web requests with aiohttpRun thousands of SQL queries concurrentlyCreate a map-reduce job that can process gigabytes of data concurrentlyUse threading with asyncio to mix blocking code with asyncio code about the readerFor intermediate Python programmers. No previous experience of concurrency required. about the authorMatthew Fowler has over 15 years of software engineering experience in roles from architect to engineering director. He has worked on Python codebases in the machine learning space, as well as led development of a Python-based ecommerce site with tens of millions of users.

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PostGIS in Action, Third Edition teaches you to solve real-world geodata problems. It first gives you a background in vector-, raster-, and topology-based GIS and then quickly moves into analyzing, viewing, and mapping data. You'll learn how to optimize queries for maximum speed, simplify geometries for greater efficiency, and create custom functions for your own applications. You'll also learn how to apply your existing GIS knowledge to PostGIS and integrate with other GIS tools. Fully updated to the latest versions of PostGIS and PostgreSQL, this Third Edition covers new PostGIS features including Foreign Data Wrappers, Database as a Service, parallelization of queries, and new JSON and Vector Tiles functions that help in creating web mapping applications. Key Features · An introduction to spatial databases · Geometry, geography, raster, and topology spatial types, · functions, and queries · Applying PostGIS to real-world problems · Extending PostGIS to web and desktop applications · Updated for PostGIS 3 and PostgreSQL 12 For readers familiar with relational databases and basic SQL. About the technology Processing location and topology data requires specialized know-how. PostGIS is a free spatial database extender for PostgreSQL that delivers the features and firepower you need to take on nearly any geodata task. With it, you can easily create location-aware queries in just a few lines of SQL code and build the back end for a mapping, raster analysis, or routing application with minimal effort. Regina Obe and Leo Hsu are database consultants and authors. Regina is a member of the PostGIS core development team and the Project Steering Committee.

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The big challenge for a successful AI project isn’t deciding which problems you can solve. It’s deciding which problems you should solve. In Managing Successful AI Projects, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage start-ups, and other business across multiple industries. Key Features · Selecting the right AI project to meet specific business goals · Economizing resources to deliver the best value for money · How to measure the success of your AI efforts in the business terms · Predict if you are you on the right track to deliver your intended business results For executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required. About the technology Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Managing Successful AI Projects sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals. Veljko Krunic is an independent data science consultant who has worked with companies that range from start-ups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.

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If you think “Modern” and “C” don’t belong in the same sentence, think again. The C standards committee actively reviews and extends the language, with updated published C standards as recently as 2018. In Modern C, author Jens Gustedt teaches you the skills and features you need to write relevant programs in this tried-and-true language, including Linux and Windows, device drivers, web servers and browsers, smartphones, and much more! Modern C teaches you to take your C programming skills to new heights, whether you’re just starting out with C or have more extensive experience. Organized by level, this comprehensive guide lets you jump in where it suits you best while still reaping the maximum benefits. Key Features Fundamentals of C programming Operators and functions Performance techniques C library functions For more than four decades, C has proved its usefulness and staying power for everything from massive systems for mainframes, satellites, and internet browsers to tiny embedded systems in vending machines, credit card readers, and alarm clocks. Today you’ll find lightning-fast, elegant C programs running in smartphone kernels, databases, 3D movies, video games, and scores of other modern applications. For programmers comfortable writing simple programs in a language like Java, Python, Ruby, C#, C++, or C. Jens Gustedt has been a senior scientist at the French National Institute for Computer Science and Control (INRIA) since 1998, working in areas including algorithms, scientific experimentation, models for coarse-grained parallelism, and distributed locking. Currently, he’s conducting the Modular C project, which has given rise to libraries such as arbogast and EiLck.

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"Do you want to learn how to creat your own virtual agent? If so, have I got the book for you!" - Thomas Joseph Heiman Design, develop, and deploy human-like AI solutions that chat with your customers, solve their problems, and streamline your support services. Creating Virtual Assistants: Chatbots that work teaches you to create the kind of AI-enabled chatbots that are revolutionizing the customer service industry. You'll learn to build effective conversational AI that can automate common inquiries and easily address your customers' most common problems. In Creating Virtual Assistants, you will learn how to: Pick the right virtual assistant type and channel for your needsWrite dialog with intentional tone and specificityTrain a virtual assistant classifier from the ground upCreate question-and-direct-response assistantsDesign and optimize a process flow for web and voiceTest your assistant's accuracy and plan out improvements This engaging and entertaining book delivers the essential technical and creative skills for designing successful virtual assistants, from coding process flows and training machine learning, to improving your written dialog. about the technologyVirtual assistants multiply the effectiveness of customer-facing communication. Great chatbots automate common tasks like gathering information, responding to questions, and improving product selections so human staff can work on more challenging problems. These powerful tools range from event classifiers that can act on an email message to conversational assistants that actually talk to your users! about the bookCreating Virtual Assistants is a hands-on guide to building custom virtual assistants for a wide variety of use cases. You'll dive right into developing an assistant capable of identifying top user requests and making a suitable response for each request type. Once you've got the basics, you'll master a multi-step process flow that can be easily adapted for both text and voice assistants. You'll learn how to hone your assistant by writing better dialog, training and improving its underlying machine learning, and spotting issues like abandonment and underperformance. By the time you're done, you'll be ready to deploy anything from a simple chatbot to a complex call center tool. about the readerFor software developers. Examples in Watson Assistant and in Python. about the authorAndrew R. Freed is a Master Inventor and Senior Technical Staff Member at IBM. He's the co-host of the AI Zen podcast. Andrew has extensive experience deploying high-volume virtual assistants and has been working in AI solutions since 2012.

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"This book will teach you everything you need to build and deploy kubernetes native microservices in a super productive fashion: a must!" - Alain Lompo Build fast, efficient Kubernetes-based Java applications using the Quarkus framework, MicroProfile, and Java standards. Most popular Java frameworks, like Spring, were designed long before the advent of Kubernetes and cloud-native systems. A new generation of tools, including Quarkus and MicroProfile have been cloud-native and Kubernetes-aware from the beginning. Kubernetes Native Microservices: With Quarkus and MicroProfile teaches you how to create efficient enterprise Java applications that are easy to deploy, maintain, and expand. In Kubernetes Native Microservices: With Quarkus and MicroProfile you'll learn how to: Deploy enterprise Java applications on KubernetesDevelop applications using the Quarkus runtime frameworkCompile natively using GraalVM for blazing speedCreate efficient microservices applicationsTake advantage of MicroProfile specifications about the technologyModern enterprise Java applications have embraced microservices, containers, Kubernetes, and a cloud-native approach. The Quarkus framework, developed at Red Hat, is designed for this deployment model, making cloud-native application development in Java much easier than it is on Spring or other older platforms. Quarkus applications compiled natively to GraalVM can deliver extremely fast runtimes, potentially improving performance by 100x or more. about the bookKubernetes Native Microservices: With Quarkus and MicroProfile introduces a modern approach to enterprise Java development using new tools designed for cloud-native applications. This book begins by exploring the impact Kubernetes and cloud systems have on your application design. Then, it quickly guides you through setting up an application using MicroProfile APIs, Kubernetes, and Quarkus. Using carefully selected examples and crystal-clear explanations, it guides you step by step from design to deployment. Written by veteran Java developers John Clingan and Ken Finnigan, this book oozes with experience and insight. You'll look past the ideal-but-untested advice you find in many books on new technologies and see how Kubernetes-native development with Quarkus and MicroProfile work in the real world. about the readerWritten for intermediate Java developers comfortable with Java EE, Jakarta EE, or Spring. Assumes some prior exposure to Docker and Kubernetes. No experience with Quarkus or MicroProfile required. about the authorJohn Clingan is a senior principal product manager at Red Hat. At Red Hat John works on next generation platforms including Quarkus. He is a co-founder of Eclipse MicroProfile, MicroProfile committer, and MicroProfile co-lead. Ken Finnigan is a senior principal software engineer and Eclipse MicroProfile technical architect at Red Hat. He's the co-founder of Eclipse MicroProfile and a MicroProfile committer.

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Building and training deep learning models from scratch is costly, time-consuming, and requires massive amounts of data. To address this concern, cutting-edge transfer learning techniques enable you to start with pretrained models you can tweak to meet your exact needs. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre takes you hands-on with customizing these open source resources for your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results even when working with limited label data, all while saving on training time and computational costs. about the technologyTransfer learning enables machine learning models to be initialized with existing prior knowledge. Initially pioneered in computer vision, transfer learning techniques have been revolutionising Natural Language Processing with big reductions in the training time and computation power needed for a model to start delivering results. Emerging pretrained language models such as ELMo and BERT have opened up new possibilities for NLP developers working in machine translation, semantic analysis, business analytics, and natural language generation. about the book Transfer Learning for Natural Language Processing is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models. Written by DARPA researcher Paul Azunre, this practical book gets you up to speed with the relevant ML concepts before diving into the cutting-edge advances that are defining the future of NLP. You’ll learn how to adapt existing state-of-the art models into real-world applications, including building a spam email classifier, a movie review sentiment analyzer, an automated fact checker, a question-answering system and a translation system for low-resource languages. what's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Foundations for exploring NLP academic literature about the readerFor machine learning engineers and data scientists with some experience in NLP. about the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. He founded Algorine Inc., a Research Lab dedicated to advancing AI/ML and identifying scenarios where they can have a significant social impact. Paul also co-founded Ghana NLP, an open source initiative focused using NLP and Transfer Learning with Ghanaian and other low-resource languages. He frequently contributes to major peer-reviewed international research journals and serves as a program committee member at top conferences in the field.

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Critical business applications worldwide are written in the versatile C# language and the powerful .NET platform, running on desktops, cloud systems, and Windows or Linux servers. Code Like a Pro in C# makes it easy to turn your existing abilities in C# or another OO language (such as Java) into practical C# mastery. There’s no “Hello World” or Computer Science 101 basics—you’ll learn by refactoring an out-of-date legacy codebase, using new techniques, tools, and best practices to bring it up to modern C# standards. Each technique is reinforced through coding katas and clean code best practices, locking in the skills you’ll need to tackle any job and ace your next interview. about the technologyC# and the .NET framework are prized for their power, flexibility, and ease of scaling. Line-of-business applications, web systems running ASP.NET, and backend servers powering enterprise data centers all rely on C#. Growing into a C# pro requires practice in real-world systems, where you learn to refactor messy code and integrate new features into existing designs. Code Like a Pro in C# lays out a path to C# mastery, tackling common challenges such as clean coding, idiomatic C#, and .NET Core that can stump aspiring C# software engineers. about the book Code like a Pro in C# builds on your existing programming skills to help you seamlessly upskill your coding practice or transition to C# from Java or another OO language. You’ll learn to write the kind of idiomatic C# code that’s essential for enterprise development, honing your mastery with guided coding katas. This book discusses essential backend skills and puts them into practice with a common career challenge: refactoring a legacy codebase to be secure, clean, and readable. By the time you’re done, you’ll have a professional-level understanding of C# and be ready to start specializing with advanced-level resources. what's inside Test-driven development Refactoring an API to a multi-platform .NET Core service Principles of clean code Using Entity Framework Core and LINQ to query and manipulate databases about the readerFor developers experienced with object-oriented programming. No C# experience required. about the author Jort Rodenburg is a software engineer specializing in C#. He has built software for numerous fields, from financial compliance to inkjet printing. Jort has mentored and taught courses on object-oriented programming to help developers get up to speed with C# and .NET

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Build powerful, production-ready search applications using the incredible features of Elasticsearch. In ElasticSearch in Action, Second Edition you will discover: Architecture, concepts, and fundamentals of Elasticsearch Installing, configuring and running Elasticsearch and Kibana Creating an index with custom settings Data types, mapping fundamentals, and templates Fundamentals of text analysis and working with text analyzers Indexing, deleting, and updating documents Indexing data in bulk and reindexing and aliasing operations Learning search concepts, relevancy scores and similarity algorithms Elasticsearch in Action, Second Edition teaches you to build scalable search applications using Elasticsearch. This completely new edition explores Elasticsearch fundamentals from the ground up. You'll deep dive into design principles, search architectures, and Elasticsearch's essential APIs. Every chapter is clearly illustrated with diagrams and hands-on examples. You'll even explore real-world use cases for full text search, data visualisations, and machine-learning. about the technology Modern search seems like magic. You type a few words and the search engine appears to know what you want. With the Elasticsearch near-real-time search and analytics engine, you can give your users this magical experience without having to do complex low-level programming or understand advanced data science algorithms. You just install it, tweak it, and get on with your work. about the book Elasticsearch in Action, Second Edition is a hands-on guide to developing fully functional search engines with Elasticsearch and Kibana. Rewritten for the latest version of Elasticsearch, this completely new second edition explores Elasticsearch's high-level architecture, reveals infrastructure patterns, and walks through the search and analytics capabilities of numerous Elasticsearch APIs. It covers dozens of awesome techniques, such as: Developing a multitude of search queries Working with various query types Enabling search results with sorting and pagination functionality Writing and working with advanced search queries Working analytics and aggregations Developing high level visualizations in Kibana Configuring and scaling the clusters, and tuning performance You'll quickly progress from the basics of installation and configuring clusters, to indexing documents, advanced aggregations, and putting your servers into production. By the time you're done, you'll be ready to build amazing search engines for your clients that take advantage of Elasticsearch's modern features. about the author Madhusudhan Konda is a full-stack lead engineer, architect, mentor, and conference speaker. He delivers live online training on Elasticsearch and the Elastic Stack.

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