Bøger udgivet af Manning Publications

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.

When youGÇÖre new to Python, it can be tough to understand where and how to use its many language features. ThereGÇÖs a dizzying array of libraries, and itGÇÖs challenging to fit everything together. The Well-Grounded Python Developer builds on Python skills youGÇÖve learned in isolation and shows you how to unify them into a meaningful whole. As you work through this practical guide, youGÇÖll discover how all the bits of the Python ecosystem connect as you build and modify a typical web server application.

Kubernetes has changed everything about deploying applications to the cloudfor the better! Kubernetes for Developers is a clear and practical beginners guide that shows you just how easy, flexible, and cost-effective it can be to make the switch to Kubernetes deployment even for small to medium-sized applications. Youll learn how to migrate your existing apps onto Kubernetes without a rebuild, and implement modern cloud native architectures that can handle your future growth. Youll take advantage of the powerful automation tools in Google Kubernetes Engine to perform automatic checks and scaling, giving you more time to spend developing great applications!

Domain-specific languages are custom text orgraphical interfaces that allow domain experts to create and modify their own software systems. With a syntax that's clear and familiar to the non-technical user, DSLs are precise enough to generate working software in traditional codewith. Written for developers who need to create user-facing DSLs, Domain-SpecificLanguages Made Easy unlocks clear and practical methods to create DSLswith easy-to-use interfaces. By working through a detailed example of a car rental ompany, you'll see how creating a custom DSL can get rid of time-consuming and bureaucratic code adjustments, freeing you up to work on features whilst your clients and colleagues write their software themselves!

An effective DBA is an efficient DBA. And if you work with SQL Server, dbatools is a lifesaver. With over 500 commands, this free and open source PowerShell module has the horsepower to automate just about every task you can imagineGÇöand then some! Written by dbatools creator Chrissy LeMaire and dbatools advocate Rob Sewell, Learn dbatools in a Month of Lunches teaches you techniques that will make you more effectiveGÇöand efficientGÇöthan you ever thought possible.

Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner.In Automated Machine Learning in Action you will learn how to:Improve a machine learning model by automatically tuning its hyperparametersPick the optimal components for creating and improving your pipelinesUse AutoML toolkits such as AutoKeras and Keras TunerDesign and implement search algorithms to find the best component for your ML taskAccelerate the AutoML process with data-parallel, model pre-training, and other techniquesAutomated Machine Learning in Action reveals how premade machine learning components can automate time-consuming ML tasks.Its written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. Youll use tools like AutoKeras to create pipelines that automatically select the best approach for your task, remove the burden of manual tuning, and can even be implemented by machine learning novices!

When it comes to data analytics, itpays to think big. PySpark blends the powerful Spark big data processing engine with the Python programming language to provide a data analysis platform that can scale up for nearly any task. Data Analysis with Python and PySparkis your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build lightning-fast pipelines for reporting, machine learning, and otherdata-centric tasks. No previous knowledge of Spark is required.