Bøger udgivet af Manning Publications

Master Java persistence using the industry-leading tools Spring Data and Hibernate.In Java Persistence with Spring Data and Hibernate you will learn: Mapping persistent classes, value types, and inheritance Mapping collections and entity associations Processing transactions with Spring Data and Hibernate Creating fetch plans, strategies, and profiles Filtering data Building Spring Data REST projects Using Java persistence with non-relational databases Querying JPA with QueryDSL Testing Java persistence applications Java Persistence with Spring Data and Hibernate teaches you the ins-and-outs of Java persistence with hands-on examples using Spring Data, JPA, and Hibernate. The book carefully analyzes the capabilities of the major Java persistence tools, and guides you through the most common use cases. By comparing and contrasting the alternatives, you’ll find it easy to choose the right tool choice for your applications. You’ll learn how to make and utilize mapping strategies, about the different approach to transactions for both Hibernate and Spring Data, and even how to efficiently test Java persistence applications. The practical techniques are demonstrated with both relational and non-relational databases. Forewords by Dmitry Aleksandrov and Mohamed Taman. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Effectively managing application data is essential for any serious application. Spring Data and Hibernate bridge the gap between object-oriented code and relational data stores, radically simplifying Java persistence. By implementing the Java Persistence API (JPA) standard, these powerful tools help you avoid common bugs related to state and application data storage. About the book Java Persistence with Spring Data and Hibernate explores Java persistence using industry-standard tools. Hands-on examples introduce object-relational mapping and guide you through different mapping strategies to suit your needs. Covering transactions, persistent application testing, and non-relational databases, this book is your go-to resource for managing data in Java applications. What's inside Mapping persistent classes, value types, and inheritance Creating fetch plans, strategies, and profiles Building Spring Data REST projects Querying JPA with QueryDSL About the reader For intermediate Java programmers. About the author Catalin Tudose has more than 20 years of experience in the Java community. Christian Bauer, Gavin King, and Gary Gregory are the authors of Java Persistence with Hibernate, Second Edition, on which this book is based. Table of Contents PART 1 - GETTING STARTED WITH ORM 1 Understanding object/relational persistence 2 Starting a project 3 Domain models and metadata 4 Working with Spring Data JPA PART 2 - MAPPING STRATEGIES 5 Mapping persistent classes 6 Mapping value types 7 Mapping inheritance 8 Mapping collections and entity associations 9 Advanced entity association mappings PART 3 - TRANSACTIONAL DATA PROCESSING 10 Managing data 11 Transactions and concurrency 12 Fetch plans, strategies, and profiles 13 Filtering data PART 4 - BUILDING JAVA PERSISTENCE APPLICATIONS WITH SPRING 14 Integrating JPA and Hibernate with Spring 15 Working with Spring Data JDBC 16 Working with Spring Data REST PART 5 - BUILDING JAVA PERSISTENCE APPLICATIONS WITH SPRING 17 Working with Spring Data MongoDB 18 Working with Hibernate OGM PART 6 - WRITING QUERIES AND TESTING JAVA PERSISTENCE APPLICATIONS 19 Querying JPA with Querydsl 20 Testing Java persistence applications

Create bulletproof, high-performance web apps and servers with Rust.In Rust Web Development you will learn:Handling the borrow checker in an asynchronous environmentCreating web APIs and using JSON in RustGraceful error handlingTesting, tracing, logging, and debuggingDeploying Rust applicationsEfficient database accessRust Web Development is a hands-on guide to building server-based web applications with Rust. If youve built web servers using Java, C#, or PHP, youll instantly fall in love with the performance and development experience Rust delivers. This book shows you how to work efficiently using pure Rust, along with important Rust libraries such as tokio for async runtimes, warp for web servers and APIs, and reqwest to run external HTTP requests.

You know how to build Go programsGÇönow 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.Bootstrapping Go is a hands-on guide to shipping Go-based software. Following examples in the powerful Go programming language, youGÇÖll 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 teamGÇÖs build and reacting with agility to customer demands. 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 customerGÇÖs needs.

Securing Kubernetes Secrets is a compact hands-on guide to storing and protecting sensitive data in a Kubernetes environment. YouGÇÖll learn a security-first mindset that is vital for storing and using secrets correctly, and tools and concepts that will help you manage sensitive assets such as certificates, keys, and key rotation. Throughout, youGÇÖll tinker with relevant real-world examples. Protect secrets stored in a source code repository and consume them in a Kubernetes cluster, deploy HashiCorp Vault to secure KubernetesGÇÖs underlying infrastructure components and applications running on the platform, along with adding layers of security to maintain protection even in the event of a partial breach. By the time youGÇÖre finished, youGÇÖll have a toolbox of techniques that you can apply both as a developer or operations professional.

Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. YouGÇÖll peer inside the GÇ£black boxGÇ¥ to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. YouGÇÖll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. YouGÇÖll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, youGÇÖll be glad you can quickly identify and fix problems.

In Effective Software Testing you will learn how to: Engineer tests with a much higher chance of finding bugs Read code coverage metrics and use them to improve your test suite Understand when to use unit tests, integration tests, and system tests Use mocks and stubs to simplify your unit testing Think of pre-conditions, post-conditions, invariants, and contracts Implement property-based tests Utilize coding practices like dependency injection and hexagonal architecture that make your software easier to test Write good and maintainable test code Effective Software Testing teaches you a systematic approach to software testing. YouGÇÖll master easy-to-apply techniques to create strong test suites that are specifically engineered for your code. Following real-world use cases and detailed code samples, youGÇÖll soon be engineering tests that find the bugs hiding in edge cases and the parts of code you would never think of testing! Along the way, youGÇÖll develop an intuition for testing that can save years of learning by trial and error. Go beyond unit tests! Great software testing makes the entire development process more efficient, from understanding your code before you write it to catching bugs in tricky corner cases.Effective Software Testing teaches you a systematic approach to software testing. YouGÇÖll master easy-to-apply techniques to create strong test suites that are specifically engineered for your code. Following real-world use cases and detailed code samples, youGÇÖll soon be engineering tests that find the bugs hiding in edge cases and the parts of code you would never think of testing! Along the way, youGÇÖll develop an intuition for testing that can save years of learning by trial and error.

Develop your NLP skills from scratch! This revised bestseller now includes coverage of  the latest Python packages, Transformers, the HuggingFace packages, and chatbot frameworks.Natural Language Processing in Action has helped thousands of data scientists build machines that understand human language. In this new and revised edition, you’ll discover state-of-the art NLP models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more. As you go, you’ll create projects that can detect fake news, filter spam, and even answer your questions, all built with Python and its ecosystem of data tools. Natural Language Processing in Action, Second Edition is your guide to building software that can read and interpret human language. This new edition is updated to include the latest Python packages and comes with full coverage of cutting-edge models like BERT, GPT-J and HuggingFace transformers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Build powerful, production-ready search applications using the incredible features of Elasticsearch.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. 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Spot errors in your Go code you didnGÇÖt even know you were making and boost your productivity by avoiding common mistakes and pitfalls. 100 Go Mistakes: How to Avoid Them shows you how to: Dodge the most common mistakes made by Go developers Structure and organize your Go application Handle data and control structures efficiently Deal with errors in an idiomatic manner Improve your concurrency skills Optimize your code Make your application production-ready and improve testing quality 100 Go Mistakes: How to Avoid Them puts a spotlight on common errors in Go code you might not even know youGÇÖre making. YouGÇÖll explore key areas of the language such as concurrency, testing, data structures, and moreGÇöand learn how to avoid and fix mistakes in your own projects.

The skills and knowledge you need to grow from a solo coder into a productive member of a software development team, along with seasoned advice on everything from effective refactoring to acing an interview. In Code Well with Others: Be an effective team player you will learn: The skills you need to succeed on a software development team Good coding practices for writing maintainable code Techniques for testing, commenting, and maintaining code others will use and read Refactoring code you didnGÇÖt write How to be a tech leader What to expect from a technical interview process Getting around gatekeeping in the tech community Slinging excellent code is only part of what it takes to succeed as a software developer. You also need to know how to work effectively on a dev team. Code Well with Others is a best practices guide for succeeding on a software development team. This unique experience-driven book explores the essential skills that you might not have learned as a solo coder, such as writing maintainable code, unit testing, and refactoring. ItGÇÖs also full of timeless advice that youGÇÖll use throughout your career, from aceing tech interviews to setting yourself up for team leadership.

Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, youGÇÖll master scalable techniques for data storage, computation, experiment tracking, and orchestration. YouGÇÖll also learn how to collaborate with data scientists to deliver exactly what they need to succeed. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science.

This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, simple language, and coverage of the latest algorithms.In TensorFlow 2.0 in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. You'll develop a sentiment analyzer for movie reviews, an NLP spam classifier, and other hands-on projects.

Use the Infrastructure as Code (IaC) approach to automate, test, and streamline infrastructure for mission-critical applications.In Essential Infrastructure as Code youwill learn how to:Optimize infrastructure for modularity and isolate dependenciesTest infrastructure configurationMitigate, troubleshoot, and isolate failed infrastructure changesCollaborate across teams on infrastructure developmentUpdate infrastructure with minimal downtime using blue-green deploymentsScale infrastructure systems supporting multiple business unitsUse patterns for provisioning tools, configuration management, and image buildingDeliver secure infrastructure configuration to productionEssential Infrastructure as Code teaches you how to automate software infrastructure by capturing your desired configurations as a set of scripts.Youll learn how to create, test, and deploy infrastructure components in a way thats easy to scale and share across an entire organization. While the patterns and techniques are tool agnostic, youll appreciate the easy-to-follow examples in Python and Terraform. A system administrator or infrastructure engineer will learn essential software development practices for managing IaC, while developers will benefit from in-depth coverage of assembling and monitoring infrastructure as part of DevOps culture.

AI models can become so complex that even experts have difficulty understanding themand forget the nuances of a cluster of novel algorithms to a business stakeholder! Fortunately, there are techniques and best practices that will help make your AI systems transparent and interpretable. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Focused on practical methods that you can implement with Python, it teaches you to open up the black box of machine learning so that you can combat data leakage and bias, improve trust in your results, and ensure compliance with legal requirements. Youll learn to identify when to utilize models that are inherently transparent, and how to mitigate opacity when youre facing a problem that demands the predictive power of ahard-to-interpret deep learning model.