Search results for ""manning""

DESCRIPTION Whether handling big data, building cloud-based services, or developing multi-tenant web applications, it's vital to have a fast, reliable search solution. Apache Solr is a scalable and ready-to-deploy open-source full-text search engine powered by Lucene. It offers key features like multi-lingual keyword searching, faceted search, intelligent matching, and relevancy weighting right out of the box. Solr in Action is the definitive guide to implementing fast and scalable search using Apache Solr 4. It uses well-documented examples ranging from basic keyword searching to scaling a system for billions of documents and queries. Readers will gain a deep understanding of how to implement core Solr capabilities such as faceted navigation through search results, matched snippet highlighting, field collapsing and search results grouping, spell checking, query auto-complete, querying by functions, and more. RETAIL SELLING POINTS Clearly-written comprehensive guide In-depth coverage of Solr 4 Uses real-world examples backed by years of experience AUDIENCE This book assumes some knowledge of Java and standard database technology. No prior experience with Solr or Lucene is required. ABOUT THE TECHNOLOGY Solr 4 provides new features to enable large-scale distributed search solutions that can be deployed as an elastically scaling cloud-based service and can provide additional intelligence to other big data technologies like Hadoop and Mahout.

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DESCRIPTION Ruby on Rails is an open source web framework. It's famously simple to build a basic Rails application. Once you've started down the track, however, you discover that Rails is a powerful, deep, and rich full-stack platform capable of tackling virtually any development challenge. Rails 4 in Action is a fully-revised second edition of Rails 3 in Action. This hands-on, comprehensive introduction to Rails gives readers everything they need to become competent and confident Rails developers. It shows how to develop a ticket tracking application that includes RESTful routing, authentication and authorization, file uploads, email, and more. It explores powerful features like designing APIs and building a Rails engine. The book also provides Test Driven Development and Behavior Driven Development in action throughout the book—just like in a top Rails shop. RETAIL SELLING POINTS Fully updated for Rails 4 New and improved testing examples More options for creating APIs Written by Rails community leaders AUDIENCE It is helpful for readers to have a background in Ruby, but no prior Rails experience is needed. ABOUT THE TECHNOLOGY Ruby on Rails, often called Rails, is an open source web framework for the Ruby programming language. It’s a full-stack platform capable of tackling virtually any development challenge.

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Revolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size. Data Mesh in Action reveals how this ground breaking architecture looks for both small start-ups and large enterprises. You'll see a datamesh in action as you explore both an extended case study andmultiple real-world examples. As you go, you'll be expertly guidedthrough discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-productsystem.

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Well-designed applications run more efficiently, have fewer bugs, and are easier to revise and maintain. Learn the fundamentals of Object-Oriented Design by investigating good and bad code.Using an engaging before-and-after approach, Object-Oriented Software Design in C++ shows you exactly what bad software looks like and how to fix it with good design principles and patterns. In it, you''ll find: Design-code-test iterations that improve code with each revision Gathering requirements to make sure you''re developing the right application Design principles like encapsulation and delegation that solve programming problems Design patterns including Observer Design Pattern that fix architecture issues Using recursion and multithreading to simplify common solutions

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In 2017, consumers downloaded 178 billion apps, and analysts predict growth to 258 billion by 2022. Mobile customers are demanding more—and better—apps, and it’s up to developers like you to write them! Flutter, a revolutionary new cross-platform software development kit created by Google, makes it easier than ever to write secure, high-performance native apps for iOS and Android. Flutter in Action teaches you to build awesome, full-featured mobile applications with Flutter. Author Eric Windmill walks with you every step of the way as you build apps that get you coding as you learn. With the engaging hands-on examples, you’ll create a basic user interface, learn about state management, and integrate a database with a Dart web app. Key features • Understanding the basic Flutter UI • Integrating platform-specific functionality for iOS and Android • Building layouts in Flutter • Testing and debugging Audience For developers familiar with the basics of programming web applications. No experience with Dart or Flutter needed! About the technology Flutter apps are blazingly fast because this open source solution compiles your Dart code to platform-specific programs with no JavaScript bridge! Flutter also supports hot reloading to update changes instantly. And thanks to its built-in widgets and rich motion APIs, Flutter’s apps are not just highly responsive, they’re stunning! Eric Windmill is a professional Dart developer, a contributor to opensource Flutter projects, and the author of FlutterByExample.com. His work is featured on Flutter’s own showcase page, https://flutter.io/showcase.

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Kubernetes Quickly is a clear and practical beginner's 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. Kubernetes Quickly is a hands-on guide to taking your first steps into Kubernetes using the powerful Google Kubernetes Engine service. It lays out a map for taking an application, containerizing it, and then deploying it onto Kubernetes. You'll learn best practice techniques for a stable and long-term Kubernetes deployment, including scaling and capacity planning, saving money by optimizing resource consumption, and tricks to make your day-to-day monitoring easier such as debugging code in the cloud. You don't need to incur huge costs or have the manpower of an enterprise organization to get a productivity boost from Kubernetes. By organizing your application component into containerized components and automating tasks like scaling and replication, Kubernetes keeps your apps running smoothly. Cloud-based Kubernetes services like Google Kubernetes Engine(GKE) reduce OS issues, simplify operations, and give you the freedom to use whatever software stack you want. If you've heard that switching to Kubernetesis complex, good news—Kubernetes for Developers will show you how it can be done without a time-consuming rebuild. Using examples from the Google Kubernetes Engine created by the team who invented Kubernetes itself, you'll learn to set up future-proof application deployments that scale to handle ever-growing and complex workloads.

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Description Microservices promise a better way to sustainably deliver business impact. Rather than a single monolithic unit, applications built in this style are composed from loosely-coupled, autonomous services. Microservices in Action is a practical book about building and deploying microservice-based applications. Written for developers and architects with a solid grasp of service-oriented development, it tackles the challenge of putting microservices into production. Key features · Review of microservice architecture · Written by authors with daily, hands-on experience · Building a delivery pipeline for microservices Audience Readers should be intermediate developers with some knowledge of enterprise application architecture. About the technology Microservices change the dev process, maximizing the efficiency and independence of small teams. To be successful, developers, team leaders, and architects need to master both the implementation details and the big picture of how microservices work in a production environment. Morgan Bruce and Paulo A. Pereira have years of experience building distributed applications, with particular expertise in the high-stakes finance and identity verification industries. They work daily with microservices in a production environment using the tools and techniques presented in this book.

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Windows or Mac OS X users are often daunted by the Linux operating system. And yet learning Linux doesn't have to be hard and the payoff is great. Linux is secure, flexible, and free. It’s less susceptible to malicious attacks, and when it is, patches are available quickly. And best of all, Linux allows users access to different desktop interfaces and loads of software, almost all of it completely free. Learn Linux in a Month of Lunches shows readers how to install and use Linux for all the things they need to do, like connecting to a network, installing software, and securing your system. Written for readers who are curious about Linux or have to get up and running for a job. This book concentrates on the tasks Linux users need to know how to do. In easy-to-follow lessons designed to take an hour or less, readers learn how to use the command line, along with practical topics like installing software, customizing your desktop, printing, and even basic networking. Readers will find a road map to the commands and processes they need to be instantly productive. Key Features: • Go from Linux novice to expert in just one month • Short, easy-to-follow lessons • Teaches the must-know commands and processes • Covers the most useful Linux topics AUDIENCE This book is for anyone looking to learn how to use Linux ABOUT THE TECHNOLOGY Linux is a free, popular, widely supported, open-source operating system based on UNIX.

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Developing applications in a reactive style ensures that the experience is always responsive. Akka.NET is a framework for building distributed, message-driven applications which are able to stay responsive for the user even in the face of failure or when faced with more users. It makes it easy for .NET developers to write applications which are able to react to changes in their environment. Reactive Applications with Akka.NET begins with an overview of reactive and a sample application written in the reactive style. Readers will learn concepts of the actor model and what these mean in a realworld reactive context. This hands-on book builds on fundamental concepts that teach how to create reliable and resilient applications. It also teaches useful Akka.NET features for building real-world applications. By the end of the book, readers will be able to look at a problem domain and understand how to create applications which are able to withstand modern demands. Key features: • Hands-on examples that build on fundamental concepts • Building real-world applications with Akka.NET • Designing an Internet of Things architecture with reactive in mind • How to create applications that can withstand modern demands AUDIENCE Readers should be comfortable with C# or F# and the .NET framework. No previous reactive experience needed. ABOUT THE TECHNOLOGY Developing applications in a reactive style ensures that the experience is always responsive. Akka.NET is a framework for building distributed, message-driven applications which are able to stay responsive for the user even in the face of failure or when faced with more users.

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Pandas has rapidly become one of Python's most popular data analysis libraries. With pandas you can efficiently sort, analyze, filter and munge almost any type of data. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. about the technologyAnyone who’s used spreadsheet software will find pandas familiar. While its column-based grids might remind you of Excel or Google Sheets, pandas is more flexible and far more powerful. It can efficiently perform operations on millions of rows and be used in tandem with other Python libraries for statistics, machine learning, and more. And best of all, using pandas doesn’t mean sacrificing user productivity or needing to write tons of complex code. It’s clean, intuitive, and fast. about the book Pandas in Action makes it easy to dive into Python-based data analysis. You’ll learn to use pandas to automate repetitive spreadsheet functionality and derive insight from data by sorting columns, filtering data subsets, and creating multi-leveled indices. Each chapter is a self-contained tutorial, letting you dip in when you need to troubleshoot tricky problems. Best of all, you won’t be learning from sterile or randomly created data. You’ll start with a variety of datasets that are big, small, incomplete, broken, and messy and learn how to clean and format them for proper analysis. what's inside Import a CSV, identify issues with its data structures, and convert it to the proper format Sort, filter, pivot, and draw conclusions from a dataset and its subsets Identify trends from text-based and time-based data Organize, group, merge, and join separate datasets Real-world datasets that are easy to download and explore about the readerFor readers experienced with spreadsheet software who know the basics of Python. about the author Boris Paskhaver is a software engineer, Agile consultant, and educator. His six programming courses on Udemy have amassed 236,000 students, with an average course rating of 4.59 out of 5. He first used Python and the pandas library to derive a variety of business insights at the world’s #1 jobs site, Indeed.com.

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Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimisation for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimisation using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.

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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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This friendly, fast-paced guide is perfect for anyone puzzled by the cloud! Learn the fundamentals of Amazon Web Services, and be ready to ace your AWS Certified Cloud Practitioner Exam. In AWS for Non-engineers you will learn: How cloud computing and AWS are different from “legacy” systems Prepare for the AWS Certified Cloud Practitioner Exam When cloud computing is the right option for your organization Core AWS services including storage services, database services, and security services How billing and pricing work on AWS, and how to pick for your budget Security and compliance concepts for building in AWS AWS for Non-engineers is written for anyone just starting with Amazon Web Services or cloud computing in general. It's written by Hiroko Nishimura, and is based on her acclaimed video course that has been taken by over 250,000 learners. In this reader-friendly book, you'll learn how to talk about cloud concepts with engineers, what the cloud could do for your business, and how to start using AWS's amazing services for your own IT tasks. When you're finished, you'll be comfortable with the basics of cloud computing on AWS and you'll be prepared to take the AWS Certified Cloud Practitioner Exam! about the technology Modern IT systems run in the cloud. Whether you're in customer service, marketing, or a technical role, cloud technologies like Amazon Web Services (AWS) have become as important to your job as spreadsheets, CRMs, and databases. Knowing AWS fundamentals will help you speak the language of developers and software engineers, and eventually create your own products, services, and projects.

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Effective Software Testing is a hands-on guide to creating high quality tests, from your first line of code through pre-delivery checks. It's full of techniques drawn from proven research in software engineering. You'll learn to efficiently engineer tests specifically for your software and end reliance on generic testing practices that may be right for every project. Each chapter puts a new technique into practice with source code samples, real-world tradeoffs, and answers to the common questions developers pose about testing. You'll learn how to scrutinize your requirements for potential tests, generate tests from your code structure, and engineer rigorous suites of unit, integration, and system tests. 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. You'll 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, you'll 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, you'll develop an intuition for testing that can save years of learning by trial and error.

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Explore the fascinating and rich world of Secret Key cryptography! This book provides practical methods for encrypting messages, an interesting and entertaining historical perspective, and an incredible collection of ciphers and codes—including 30 unbreakable methods. In Secret Key Cryptography: Ciphers, from simple to unbreakable you will: Measure the strength of your ciphers and learn how to guarantee their security Construct and incorporate data-compression codes Generate true random numbers in bulk Construct huge primes and safe primes Add an undetectable backdoor to a cipher Defeat hypothetical ultracomputers that could be developed decades from now Construct 30 unbreakable ciphers Secret Key Cryptography gives you a toolbox of cryptographic techniques and Secret Key methods. The book's simple, non-technical language is easy to understand and accessible for any reader, even without the advanced mathematics normally required for cryptography. You'll learn how to create and solve ciphers, as well as how to measure their strength. As you go, you'll explore both historic ciphers and groundbreaking new approaches—including a never-before-seen way to implement the uncrackable One-Time Pad algorithm. about the technology Secret Key cryptography is the backbone of all modern computing infrastructure. Secret Key ciphers use the same key to encrypt and decrypt messages. Properly designed, these algorithms are efficient and practical. Some Secret Key approaches are uncrackable, even under attacks backed by supercomputers or quantum technology! about the book Secret Key Cryptography teaches anyone how to create a wide range of ciphers—even if you have no background in math or creating codes. You'll combine Secret Key techniques to achieve ciphers that are effectively uncrackable and avoid common pitfalls that result in strong-looking but weak ciphers. The book reveals scores of different cipher methods, including both historic examples and current innovations in the field. RETAIL SELLING POINTS • Measure the strength of your ciphers and learn how to guarantee their security • Construct and incorporate data-compression codes • Generate true random numbers in bulk • Construct huge primes and safe primes • Add an undetectable backdoor to a cipher • Defeat hypothetical ultra computers that could be developed decades from now • Construct 30 unbreakable ciphers AUDIENCE For professional engineers, computer scientists, and cryptography hobbyists. No advanced math knowledge is required

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Master core data analysis skills using Julia. Julia for Data Analysis is a fascinating, hands-on projects guide you through time series data, predictive models, popularity ranking, and more. With this book, you will learn how to: Read and write data in various formats Work with tabular data, including subsetting, grouping, and transforming Visualise your data using plots Perform statistical analysis Build predictive models Create complex data processing pipelines Julia was designed for the unique needs of data scientists: it's expressive and easy-to-use whilst also delivering super fast code execution. Julia for Data Analysis teaches you how to perform core data science tasks with this amazing language. It is written by Bogumił Kamiński, a top contributor to Julia, #1 Julia answerer on StackOverflow, and a lead developer of Julia's core data package DataFrames.jl. You will learn how to write production-quality code in Julia, and utilize Julia's core features for data gathering, visualisation, and working with data frames. Plus, the engaging hands-on projects get you into the action quickly. About the technology Julia is a huge step forward for data science and scientific computing. It is a powerful high-performance programming language with many developer-friendly features like garbage collection, dynamic typing, just-in-time compilation, and a flexible approach to concurrent, parallel, and distributed computing. Although Julia's strong numerical programming features make it a favorite of data scientists, it is also an awesome general purpose programming language. About the reader For data scientists familiar with Python or R. No experience with Julia required.

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Learn how to build fast and secure gRPC-based microservices using the powerful Go programming language. Ideal for software developers who know the basics of Go. gRPC Microservices in Go shows you howto combine the powerful gRPC Remote Procedure Call framework with Golang's low-level efficiency and flexibility. You will learn how to develop microservice inter-service communication patterns powered by gRPC, design backwards compatible APIs, and apply hexagonal architecture to microservices. Key features: Designing and implementing a resilient microservice architecture Testing microservices Deploying microservices to the cloud with modern orchestration tools Monitoring and overseeing microservices The powerful gRPC Remote Procedure Call framework delivers superior speed and security over protocols like REST. When paired with Golang's low-level efficiency and flexibility, gRPC and Go become a killer combination for latency-sensitive microservices applications. gRPC Microservices in Go shows you how to utilise these powerful tools to build production-grade microservices. About the technology Initially developed by Google, gRPC is a high-performance messaging protocol that is extraordinarily well-suited for microservices applications. This open-source framework helps you elegantly and reliably deliver messages securely within a microservices architecture. Its seamless integration with infrastructure for load balancing, tracing, fault tolerance, and security helps you effortlessly solve many of the common challenges of microservices. gRPC's server and client stubs let your project call remote service methods and define the business logic behind them. Combined with Go's fast compilation and execution speed, it's the perfect solution for building large-scale microservices architectures.

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Build your skills with essential modern C++ features hands-on by completing eight interesting coding projects. In Learn C++ by Example you''ll: Learn how to write modern C++ using new features from C++ 11 to 23 Think about testing as you code Learn what happens under the hood of your C++ code Choose the most efficient algorithm or data structure About the technology C++ is used for everything from financial modeling to video games. C++ 11, 17, and 20 have all introduced exciting features that can increase your coding efficiency. This book introduces these features by putting them to use in real projects. Along the way, you''ll get up to speed with frequently used data structures, algorithms, and various parts of the standard library.

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Master the demand forecasting skills you need to decide what resources to acquire, products to produce, and where and how to distribute them. For demand planners, S&OP managers, supply chain leaders, and data scientists. Demand Forecasting Best Practices is a unique step-by-step guide, demonstrating forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment. You will learn how to: Lead a demand planning team to improve forecasting quality while reducing workload Properly define the objectives, granularity, and horizon of your demand planning process Use smart, value-weighted KPIs to track accuracy and bias Spot areas of your process where there is room for improvement Help planners and stakeholders (sales, marketing, finances) add value to your process Identify what kind of data you should be collecting, and how Utilise different types of statistical and machine learning models Follow author Nicolas Vandeput's original five-step framework for demand planning excellence and learn how to tailor it to your own company's needs. You will learn how to optimise demand planning for a more effective supply chain and will soon be delivering accurate predictions that drive major business value. About the technology Demand forecasting is vital for the success of any product supply chain. It allows companies to make better decisions about what resources to acquire, what products to produce, and where and how to distribute them. As an effective demand forecaster, you can help your organisation avoid overproduction, reduce waste, and optimise inventory levels for a real competitive advantage.

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Create Python packages to share your code in a scalable and maintainable way. Improve team productivity, publish helpful libraries, or even start your own open source project following the latest Python packaging standards. In Publishing Python Packages you will learn how to: Build extensions and console script commands Use tox to automate packaging, installing, and testing Build a continuous integration pipeline using GitHub Actions Improve code quality and reduce manual review using bandit, black, mypy, and radon Create published documentation for your packages Keep packages up to date with pyupgrade and Dependabot Foster an open source community using GitHub features Publishing Python Packages teaches you how to easily share your Python code with your team and the outside world. Learn a repeatable and highly automated process for package maintenance that's based on the best practices, tools, and standards of Python packaging. Whether you're entirely new to Python packaging or looking for optimal ways to maintain and scale your packages, this fast-paced and engaging guide is for you. about the technology Python packages are a great way to share your code and give a productivity boost to your colleagues and community. Whether you're reusing your code internally or contributing to open source, a properly automated system of packaging will save you from time-consuming manual maintenance. about the book Publishing Python Packages reveals best practices and standards for packaging your Python code in an easy, automated, and scalable way. The book walks you through creating a complete package, including a C extension, and guides you all the way to publishing on the Python Package Index. You'll get hands-on experience with the latest packaging tools, and learn the ins-and-outs of package testing and continuous integration. You'll even learn how to set up a successful open source project, including licensing, documentation, and nurturing a community of contributors.

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Learn Julia programming by building fun projects, like launching rockets, building password keepers, and even coding battle simulations. Julia as a Second Language covers: How Julia implements data types such as numbers, strings, arrays, and dictionaries Solving problems with both object-oriented and functional programming Getting immediate feedback with Julia's read-evaluate-print-loop (REPL) Taking advantage of Julia's powerful multiple dispatch system Sharing code using modules and packages Julia as a Second Language introduces Julia to readers with a beginning-level knowledge of another language like Python or JavaScript. It skips programming basics and dives straight into Julia's unique features. You'll learn by coding engaging hands-on projects that encourage you to apply what you are learning immediately. About the technology Julia is a powerful high-performance programming language with many developer-friendly features like garbage collection, dynamic typing, just-in-time compilation, and a flexible approach to concurrent, parallel, and distributed computing. Although Julia's strong numerical programming features make it a favorite of data scientists, it is also an awesome general purpose programming language. Julia's users call it the "goldilocks language", with a "just right" balance of performance and productivity. About the reader Readers need basic skills with another programming language like Python, JavaScript, or C#.

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Safely store secret information like passwords, keys, and certificates in Kubernetes. In this practical guide, you'll discover methods for adding layers of security that will defend the critical data of your applications.In Securing Kubernetes Secrets you will find: Strategies for storing secure assets in Kubernetes Cryptographic options and how to apply them in Kubernetes Using the HashiCorp Vault server on Kubernetes for secure secrets storage Managing security with public cloud providers Applying security concepts using tools from the Kubernetes ecosystem End-to-end secrets storage from development to operations Implementing in Kubernetes in CI/CD systems Secrets, like database passwords and API keys, are some of the most important data in your application. Securing Kubernetes Secrets reveals how to store these sensitive assets in Kubernetes in a way that's protected against leaks and hacks. You'll learn about the default capabilities of Kubernetes secrets, where they're lacking, and alternative options to strengthen applications and infrastructure. Kubernetes secrets include small and sensitive data like passwords, tokens, and keys. The security defaults of Kubernetes are rarely enough to protect this valuable data, and can leave it—and you—exposed. In this book, you'll learn how to bake security into Kubernetes-based applications right from the start with security best practices and reliable third-party tools like HashiCorp Vault

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A jargon-free, practical guide to the key concepts, terminology, and technologies of cybersecurity perfect for anyone planning or implementing a security strategy. Go behind the headlines of famous attacks and learn lessons from real-world breaches that author Tom Kranz has personally helped to clean up. Making Sense of Cyber Security is full of clear-headed advice and examples that will help you identify risks in your organization and choose the right path to apply the important security concepts effectively. Making Sense of Cyber Security is a no-nonsense overview of common cyber threats. Written for readers at all skill levels, this easy-to-read guide breaks down the core ideas and terminology of cybersecurity so that you can effectively contribute to the planning and implementation of a security strategy. You'll learn the three pillars of a successful security strategy and how to create and apply threat models that will iteratively improve your organization's readiness about the technology Every high-profile data breach underlines the need for cybersecurity. A proper understanding of threats, vulnerabilities, and most-likely attacks empowers you to make better decisions and mitigate risks. about the book Making Sense of Cyber Security is a no-nonsense overview of common cyber threats. Written for readers at all skill levels, this easy-to-read guide breaks down the core ideas and terminology of cybersecurity so that you can effectively contribute to the planning and implementation of a security strategy. You'll learn the three pillars of a successful security strategy and how to create and apply threat models that will iteratively improve your organization's readiness. Renowned security expert Tom Kranz teaches you to think like a hacker, ensuring you can spot your own weak points and evaluate risks in new technologies or processes. You'll cement this mindset by planning your own penetration test—taking on the role of the attacker in a simulated hack against an asset management firm. Finally, you'll work through a real-world security breach, learning how to respond to live threats and apply the lessons learned to your own organization.

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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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With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You’ll get familiar with Python’s functional built-ins like the functools operator and itertools modules, as well as the toolz library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you’ll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level!Key features• An introduction to functional and parallel programming • Data science workflow • Profiling code for better performance • Fulfilling different quality objectives for a single unifying task • Python multiprocessing • Practical exercises including full-scale distributed applicationsAudienceReaders should have intermediate Python programming skills.About the technologyPython is a data scientist’s dream-come-true, thanks to readily available libraries that support tasks like data analysis, machine learning, visualization, and numerical computing. J.T. Wolohan is a lead data scientist at Booz Allen Hamilton and a PhD researcher at Indiana University, Bloomington, affiliated with the Department of Information and Library Science and the School of Informatics and Computing. His professional work focuses on rapid prototyping and scalable AI. His research focuses on computational analysis of social uses of language online.

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There’s a lot more to the blockchain than mining Bitcoin. This secure system for registering and verifying ownership and identity is perfect for supply chain logistics, health records, and other sensitive data management tasks. Blockchain in Action unlocks the full potential of this revolutionary technology, showing you how to build own decentralized apps for secure applications including digital democracy, private auctions, and electronic record management. Key Features · How blockchain differs from other distributed systems · Smart contract development with Ethereum and the Solidity language · Web UI for decentralized apps · Identity, privacy and security techniques · On-chain and off-chain data storage For intermediate programmers who know the basics of object-oriented languages and have a working knowledge of JavaScript. About the technology A blockchain is a decentralized record, stored across numerous devices with no central control or authority. Copies of this shared database are constantly reconciled with one another, and records are cryptographically encoded to make them unchangeable. The result is a type of database that is at once transparent and publicly accessible, and where it is impossible to falsify or alter the historic data record. Bina Ramamurthy holds a Ph.D. in fault-tolerant distributed systems, and has thirty years of experience teaching cryptography, peer-to-peer networking, and distributed systems. She is the instructor and content creator for the University of Buffalo four-course specialization on blockchain technology on the Coursera MOOC platform, and the recipient of the 2019 SUNY Chancellor’s Award for Teaching Excellence.

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Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of automating tedious business processes and back-office tasks Consider the competitive advantage of making decisions when you know the most likely future events Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Machine Learning for Business teaches you how to make your company more automated, productive, and competitive by mastering practical, implementable machine learning techniques and tools. Thanks to the authors’ down-to-earth style, you’ll easily grok why process automation is so important and why machine learning is key to its success. In this hands-on guide, you’ll work through seven end-to-end automation scenarios covering business processes in accounts payable, billing, payroll, customer support, and other common tasks. Using Amazon SageMaker (no installation required!), you’ll build and deploy machine learning applications as you practice takeaway skills you’ll use over and over. By the time you’re finished, you’ll confidently identify machine learning opportunities in your company and implement automated applications that can sharpen your competitive edge! Key Features Identifying processes suited to machine learning Using machine learning to automate back office processes Seven everyday business process projects Using open source and cloud-based tools Case studies for machine learning decision making For technically-inclined business professionals or business developers. No previous experience with automation tools or programming is necessary. Doug Hudgeon runs a business automation consultancy, putting his considerable experience helping companies set up automation and machine learning teams to good use. In 2000, Doug launched one of Australia’s first electronic invoicing automation companies. Richard Nichol has over 20 years of experience as a data scientist and software engineer. He currently specializes in maximizing the value of data through AI and machine learning techniques.

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Description The Redux JavaScript library consolidates state in a single object, radically simplifying one of the largest sources of bugs. With Redux in Action, you'll discover how to integrate Redux into your React application and development environment, write custom middleware, and optimize for performance. Key features · Clear introduction · Hands-on examples · Step-by-step guide Audience Written for web developers comfortable with JavaScript and ES6, as well as experience using React. About the technology Whether users are passing data through several layers of components or sharing and syncing data between unrelated parts of the app, Redux makes state management a breeze. Redux promotes predictability, testability, and the ability to debug dynamic applications, letting users focus on building great apps. Author biography Marc Garreau has architected and executed half a dozen unique client-side applications leveraging Redux for state management. Will Faurot is experienced in equal parts production Redux, and mentoring Redux developers of all skill levels.

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Description Microservices break down a large application into smaller components, each interacting with each other to create a united whole. As each component can start, stop, and scale independently, so the whole system benefits from better fault-tolerance and resilience. Enterprise Java Microservices is an example-rich tutorial that shows readers how to design and manage large-scale Java applications as a collection of microservices. Key features · The microservices mental model · Fault tolerance with Netflix Hystrix · Securing your microservices · Deploying to the cloud Audience This book is for Java developers familiar with distributed n-tier application architecture. About the technology Enterprise Java is a set of APIs, and their implementations, that can provide the entire stack of an application from UI down to the database, communicate with external applications via webservices, and integrate with internal legacy systems, to name a few, with the goal of supporting the business requirements of an Enterprise. Ken Finnigan has worked as an engineer and consultant for nearly 20 years. He currently co-leads the development of WildFly Swarm for Red Hat.

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Unlike any other programming language, Haskell is purely functionalwith a strong type system and lazy evaluation. It is arguable the mostinteresting language but also has the reputation of being one of themost challenging to learn. Learning Haskell doesn't have to bedifficult, and this book can help! Get Programming with Haskell introduces you to the Haskell languagewithout drowning you in academic jargon and heavy functionalprogramming theory. By working through 43 easy-to-follow lessons,you'll learn Haskell by doing Haskell. This book starts with firstconcepts, building your knowledge with concrete examples andexercises. You'll learn to think the Haskell way, as you start tounderstand the language and how to use it effectively. And you'll reallyappreciate the crystal-clear illustrations, quick-checks, and open-endedtasks that make sure you're solid on each new concept before you movealong! KEY FEATURES• Full of interesting examples• Easy approach to functional programming basics• Write safe, predictable code with fewer bugs• Lots of illustrations and exercises Readers should have some experience with JavaScript, Python orRuby. No functional programming or math skills required. ABOUT THE TECHNOLOGYHaskell is a purely functional programming language. It treats programs asmuch like math as you can, requires you to think more than you type, andcan express the idea of infinite lists in code! These are just the more obviousfeatures that make Haskell unlike any other programming language AUTHOR BIOWill Kurt currently works as a Data Scientist at Quick Sprout. With aformal background in both Computer Science (MS) and English Literature(BA) he is fascinated with explaining complex technical topics as clearlyand generally as possible. He has taught a course section on Haskell at theUniversity of Nevada, Reno and given workshops on FunctionalProgramming. He also blogs about probability at CountBayesie.com.

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Functional programming is a way of thinking about programs that emphasizes functions, while avoiding state mutation. C# includes a number of functional features and libraries, enabling us to take advantage of these benefits. Functional Programming in C# teaches readers to apply functional thinking to real-world scenarios. They’ll start by learning the principles of functional programming, and how they translate in the C# language. By the end of this book, readers will be able to integrate functional techniques, making their C# programs robust and maintainable, and helping them to become more well rounded developers. Key Features: · Introduction to functional programming · Real-world examples · Integrate functional techniques · Become a well rounded developer This book is designed to help C# programmers with an OOP background understand functional thinking. About the Technology: Functional programming is a way of thinking about programs that emphasizes functions, while avoiding state mutation. It allows us to write elegant, intention-revealing code, that shines in testability and support for concurrency.

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DESCRIPTION Data accumulated about customers, products, and website users can not only help interpret the past, it can help predict the future! Probabilistic programming is a programming paradigm in which code models are used to draw probabilistic inferences from data. By applying specialized algorithms, programs assign degrees of probability to conclusions and make it possible to forecast future events like sales trends, computer system failures, experimental outcomes, and other critical concerns. Practical Probabilistic Programming explains how to use the PP paradigm to model application domains and express those probabilistic models in code. It shows how to use the Figaro language to build a spam filter and apply Bayesian and Markov networks to diagnose computer system data problems and recover digital images. Then it dives into the world of probabilistic inference, where algorithms help turn the extended prediction of social media usage into a science. The book covers functional-style programming for text analysis and using object-oriented models to predict social phenomena like the spread of tweets, and using open universe models to model real-life social media usage. It also teaches the principles of algorithms such as belief propagation and Markov chain Monte Carlo. The book closes out with modeling dynamic systems by using a product cycle as its main example and explains how probabilistic KEY SELLING POINTS Covers the basic rules of probabilistic inference Illustrated with useful practical examples Build a wide variety of probabilistic models AUDIENCE Code examples are written in Figaro. Some knowledge of Scala and a basic foundation in data science is helpful. No prior exposure to probabilistic programming is required. ABOUT THE TECHNOLOGY Probabilistic programming is a new discipline, and the tools and best practices are still emerging. Powerful new tools like the Figaro library built into Scala make probabilistic programming more practical in day-to-day work as a data scientist.

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DESCRIPTION Any large application needs an efficient way to handle the constant messages passing between components in the system. Billed as "messaging that just works," the RabbitMQ message broker initially appeals to developers because it's lightweight, easy to set up, and low maintenance. They stick with it because it's powerful, fast, and up to nearly anything that can be thrown at it. This book takes readers beyond the basics and explores the challenges of clustering and distributing messages across enterprise-level data-centers using RabbitMQ. RabbitMQ in Depth is a practical guide to building and maintaining message-based systems. This book covers detailed architectural and operational use of RabbitMQ with an emphasis on not just how it works but why it works the way it does. It provides examples and detailed explanations of everything from low-level communication to integration with third-party systems. It also offers insights needed to make core architectural choices and develop procedures for effective operational management. KEY FEATURES Approachable detailed resource Explains the "how" and "why" of RabbitMQ Takes readers well beyond the basics AUDIENCE Written for programmers with a basic understanding of messaging oriented systems and RabbitMQ. ABOUT THE TECHNOLOGY RabbitMQ is an open-source message broker software that programs can use to exchange messages with each other to create scalable and reliable application architectures.

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Learn Anthos directly from the Google development team! Anthos delivers a consistent management platform for deploying and operating Linux and Windows applications anywhere—multicloud, edge, on-prem, bare metal, or VMware. In Google Anthos in Action you will learn: How Anthos reduces your dependencies and stack-bloat Running applications across multiple clouds and platforms Handling different workloads and data Adding automation to speed up code delivery Modernizing infrastructure with microservices and Service Mesh Policy management for enterprises Security and observability at scale In a cloud-centric world, all deployment is becoming hybrid deployment. Anthos is a modern, Kubernetes-based cloud platform that enables you to run your software in multicloud, hybrid, or on-premises deployments using the same operations tools and approach. With powerful automation features, it boosts your efficiency along the whole development lifecycle. Google Anthos in Action demystifies Anthos with practical examples of Anthos at work and invaluable insights from the Google team that built it. about the technology Anthos is built on a simple concept: write once, and run anywhere—whether that's on-prem, in any public cloud, on the edge, or all three. As the first truly multicloud platform from a major provider, Anthos was designed with the practical goals of balancing cost, efficiency, security, and performance. Anthos lets you simplify your stack, deliver software faster with cloud-native tooling, and automatically integrate high levels of security into your deployments. about the book Google Anthos in Action comes directly from the Anthos team at Google. This comprehensive book takes a true DevOps mindset, considering Google-tested patterns for how an application is designed, built, deployed, managed, monitored, and scaled. Developers will love how having a consistent platform across clouds brings a massive performance boost by standardizing the application across deployment targets, as well as how Anthos makes it easy to modernize legacy applications to cloud native infrastructure. Operations pros will appreciate how simple it is to integrate Anthos with CI/CD pipelines, automate security and policy management, and work with enterprise-level Kubernetes. Each concept is fully illustrated with exercises and hands-on examples, so you can see the power of Anthos in action. RETAIL SELLING POINTS • How Anthos reduces your dependencies and stack-bloat • Running applications across multiple clouds and platforms • Handling different workloads and data • Adding automation to speed up code delivery • Modernizing infrastructure with microservices and Service Mesh • Policy management for enterprises • Security and observability at scale AUDIENCE For software and cloud engineers with knowledge of Kubernetes.

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Create fully featured APIs with the ASP.NET Core framework! Building Web APIs with ASP.NET Core is a practical beginner's guide to creating your first web APIs using the REST and GraphQL standards. The book is structured just like a real-world development project, with each chapter introducing a new feature request. This edition will help you develop an API that feeds web-based services, including websites and mobile apps, for a board games application. You will build your API with an ecosystem of ASP.NET Core tools that helps simplify everything from setting up your data model to generating documentation. You will learn how to: Set up your environment with VS 2022, Node, Git, and more Create an ASP.NET Core project from scratch Integrate with SQL Server Use Entity Framework Core to set up a data model Create back-end controllers Design an API to serve data Write API documentation using Swagger and Swashbuckle Consume an API using typical web client-side frameworks, including Angular and ReactJS Handle requests and routes using controllers and Minimal API About the technology APIs are the backbone of modern software and a vital skill for anyone serious about professional development. The free and open-source ASP.NET Core framework is one of the best tools available for creating APIs! It is designed to maximise code execution speed and reliability, and its “no compile” development experience means you are never stuck waiting for your code. Widely used by both small companies and big enterprises, ASP.NET Core benefits from both the support of its open-source community and the backing of Microsoft and the Azure cloud.

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A friendly, fully-illustrated introduction to the most important computer programming algorithms. The algorithms you''ll use most often as a programmer have already been discovered, tested, and proven. This book will prepare you for those pesky algorithms questions in every programming job interview and help you apply them in your day-to-day work. And if you want to understand them without slogging through dense multipage proofs, this is the book for you. In Grokking Algorithms, Second Edition you will discover: Search, sort, and graph algorithms Data structures such as arrays, lists, hash tables, trees, and graphs NP complete and greedy algorithms Performance trade-offs between algorithms Exercises and code samples in every chapter Over 400 illustrations with detailed walkthroughs The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithm

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Build professional-grade full-stack web applications using C# and ASP.NET Core. ASP.NET Core in Action, 3rd edition by Microsoft MVP Andrew Lock, is a fully updated edition, ideal for intermediate C# developers, and teaches you how to use your C# and .NET skills to build amazing cross-platform web applications. This revised bestseller reveals the latest .NET patterns, including minimal APIs and minimal hosting. Even if you have never worked with ASP.NET, you will soon start creating productive cross-platform web apps. Features include: Build minimal APIs for serving JSON to client-side applications Create dynamic, server-side rendered applications using Razor Pages User authentication and authorisation Store data using Entity Framework Core Unit and integration tests for ASP.NET Core applications Write custom middleware and components About the technology Developers love ASP.NET Core for its libraries and pre-built components that maximize productivity. This awesome web framework empowers you to build web applications using HTTP APIs, server-side rendered HTML, and long-running server processes - all by using the same familiar structure and components. Version 7.0 takes full advantage of new C# features, easy-to-build minimal APIs, and big performance upgrades.

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Take PowerShell beyond simple scripts and build time-saving automations for your team, your users, and the world. In Practical Automation with PowerShell you will learn how to: Build PowerShell functions to automate common and complex tasks Create smart automations that are adaptable to new challenges Structure your code for sharing and reusability Store and secure your automations Execute automations with Azure Automation, Azure Functions, and Jenkins Share your automations with your team and non-technical colleagues Store and retrieve data, credentials, and variables Use source control solutions to maintain and test code changes Provide front-end UI solutions for PowerShell automations Practical Automation in PowerShell reveals how you can use PowerShell to build automation solutions for a huge number of common admin and DevOps tasks. It takes you beyond scripting basics and shows you how to handle the unforeseen complexities that can keep automations from becoming reusable and resilient. You'll discover tools and platforms that let you share your automations with your team and even roll them out to non-technical users through easy-to-understand Sharepoint frontends. about the technology Most repetitive tasks you face as an administrator or DevOps engineer can be automated using PowerShell. Mastering the basics of this powerful language, along with some best practices for saving and sharing your automations, can radically improve the consistency and efficiency of your work. about the book Practical Automation with PowerShell teaches you how to build, organize, and share useful automations with PowerShell. You'll start with development skills you might not have learned as a sysadmin, including techniques to help you structure and manage your code, and common pitfalls to avoid. You'll quickly progress to essential factors for sharable automations, such as securely storing information, connecting to remote machines, and creating automations that can adapt to different use cases. Finally, you'll take your automations out into the world. You'll learn how to share them with your team or end users, and build front ends that allow non-technical people to run them at the touch of a button.

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Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning's common pitfalls and deliver adaptable model upgrades without constant manual adjustment. In Evolutionary Deep Learning you will learn how to: Solve complex design and analysis problems with evolutionary computation Tune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization Use unsupervised learning with a deep learning autoencoder to regenerate sample data Understand the basics of reinforcement learning and the Q Learning equation Apply Q Learning to deep learning to produce deep reinforcement learning Optimize the loss function and network architecture of unsupervised autoencoders Make an evolutionary agent that can play an OpenAI Gym game Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. about the technology Evolutionary deep learning merges the biology-simulating practices of evolutionary computation (EC) with the neural networks of deep learning. This unique approach can automate entire DL systems and help uncover new strategies and architectures. It gives new and aspiring AI engineers a set of optimization tools that can reliably improve output without demanding an endless churn of new data. about the reader For data scientists who know Python.

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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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"This book is a comprehensive roadmap to the most crucial fix for today's broken Internet." - Brian Behlendorf, GM for Blockchain, Healthcare and Identity at the Linux Foundation In a world of changing privacy regulations, identity theft, and online anonymity, identity is a precious and complex concept. Self-Sovereign Identity (SSI) is a set of technologies that move control of digital identity from third party “identity providers”directly to individuals, and it promises to be one of the most important trendsfor the coming decades. Now in Self-Sovereign Identity, privacy and personal data experts Drummond Reed and Alex Preukschat lay out a roadmap for a futureof personal sovereignty powered by the Blockchain and cryptography. Cutting through the technical jargon with dozens of practical use cases from experts across all major industries, it presents a clear and compelling argument for why SSI is a paradigm shift, and shows how you can be ready to be prepared forit. about the technology Trust onthe internet is at an all-time low. Large corporations and institutions control our personal data because we've never had a simple, safe, strong way to prove who we are online. Self-sovereign identity (SSI) changes all that. about the book In Self-Sovereign Identity: Decentralized digital identity and verifiable credentials, you'll learn how SSI empowers us to receive digitally-signed credentials, store them in private wallets, and securely prove our online identities. It combines a clear, jargon-free introduction to this blockchain-inspired paradigm shift with interesting essays written by its leading practitioners. Whether for property transfer, ebanking, frictionless travel, or personalized services, the SSI model for digital trust will reshape our collective future. what's inside · The architecture of SSI software and services · The technical, legal, and governance concepts behind SSI · How SSI affects global business industry-by-industry · Emerging standards for SSI about the reader For technology and business readers. No prior SSI, cryptography, or blockchain experience required. aboutthe author Drummond Reed is the Chief Trust Officer at Evernym, a technology leader in SSI. Alex Preukschat is the co-founder of SSIMeetup.org and AlianzaBlockchain.org.

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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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It's time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you’re grokking as you go. You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features · Different types of machine learning, including supervised and unsupervised learning · Algorithms for simplifying, classifying, and splitting data · Machine learning packages and tools · Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Luis G. Serrano has worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system. He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal. He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.

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Description Ruby is a dynamic language perfect for creating applications, development tools, and administrative scripts. The Well-Grounded Rubyist, Third Edition is a perfect tutorial for newcomers to Ruby and a great way for experienced Rubyists to deepen their understanding of the language. Key features · Fully updated to cover the latest in Ruby 2.5 · Clear explanations of Ruby concepts and how to apply them · Simple examples to seal in the learning · Prepares readers to use Ruby anywhere for any purpose Audience This book teaches Ruby from the ground up. The reader should have command of basic programming concepts in another language. About the technology Because it's the backbone of the well-loved Ruby on Rails web framework, Ruby has become one of the most popular programming languages in the world. David A. Black is an internationally-known Ruby developer, author, trainer, speaker, and event organizer, as well as a co-founder of Ruby Central. Joseph Leo III is a Ruby teacher, mentor, and community advocate. He is the lead organizer of the Gotham Ruby Conference (GoRuCo) and founder of Def Method.

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Turn the corner from “Haskell student” to “Haskell developer.” Haskell in Depth explores the important language features and programming skills you’ll need to build production-quality software using Haskell. And along the way, you’ll pick up some interesting insights into why Haskell looks and works the way it does. Get ready to go deep! Haskell in Depth is the perfect second book on Haskell. After a quick refresher on Haskell basics, this hands-on guide dives into examples and application scenarios designed to teach how Haskell works and how to apply it correctly. You’ll learn about managing projects with Cabal and Stack, tackle error-handling and testing, and package programs and libraries for production deployment. Key Features · Organizing your projects with Cabal and Stack · Testing and profiling · Working with data · Building web services and networking apps · Using the sophisticated libraries like lens, vinyl, and servant Written for developers familiar with Haskell basics. About the technology As software becomes more complex, it’s essential to program efficiently using tools and techniques that guarantee your applications will run correctly, grow easily, and last a long time. Haskell is a functional programming language that blends a mathematically-rigorous approach to software design with a tested ecosystem of tools and libraries you can use to build deployable applications. Since 2008, Vitaly Bragilevsky has been teaching Haskell and functional programming to undergraduate students at the Southern Federal University located in Rostov-on-Don, Russia. He is a member of the Haskell 2020 Committee, and has worked on the source code of the Glasgow Haskell Compiler (GHC) and the Idris compiler, both of which are implemented in Haskell.

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Docker's simple idea, wrapping an application and its dependencies into a single deployable package, has continued to drive a revolution in software delivery. Docker in Practice, Second Edition presents nearly 120 practical techniques, hand-picked to help users get the most out of Docker. This book is an essential resource that readers will want to have open on their desk! Key features · Hands-on guide · Step-by-step instructions · Full of practical tips Written for developers and engineers using Docker in production. About the technology Docker is a platform that allows users to “build, ship, and run any app, anywhere.” It has come a long way in an incredibly short time and is now considered a standard way of solving one of the costliest aspects of software: deployment. Author biography Ian Miell is the Lead OpenShift Architect at Barclays. Aidan Hobson Sayers is a developer at Hadean. Previously, they used Docker to transform DevOps at OpenBet.

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