The essential blueprints and workflow you need to build successful AI business applications Key Features *Learn and master the essential blueprints to program AI for real-world business applications *Gain insights into how modern AI and machine learning solve core business challenges *Acquire practical techniques and a workflow that can build AI applications using state-of-the-art software libraries *Work with a practical, code-based strategy for creating successful AI solutions in your business Book Description AI Blueprints gives you a working framework and the techniques to build your own successful AI business applications. You’ll learn across six business scenarios how AI can solve critical challenges with state-of-the-art AI software libraries and a well thought out workflow. Along the way you’ll discover the practical techniques to build AI business applications from first design to full coding and deployment. The AI blueprints in this book solve key business scenarios. The first blueprint uses AI to find solutions for building plans for cloud computing that are on-time and under budget. The second blueprint involves an AI system that continuously monitors social media to gauge public feeling about a topic of interest - such as self-driving cars. You’ll learn how to approach AI business problems and apply blueprints that can ensure success. The next AI scenario shows you how to approach the problem of creating a recommendation engine and monitoring how those recommendations perform. The fourth blueprint shows you how to use deep learning to find your business logo in social media photos and assess how people interact with your products. Learn the practical techniques involved and how to apply these blueprints intelligently. The fifth blueprint is about how to best design a ‘trending now’ section on your website, much like the one we know from Twitter. The sixth blueprint shows how to create helpful chatbots so that an AI system can understand customers’ questions and answer them with relevant responses. This book continuously demonstrates a working framework and strategy for building AI business applications. Along the way, you’ll also learn how to prepare for future advances in AI. You’ll gain a workflow and a toolbox of patterns and techniques so that you can create your own smart code. What you will learn *An essential toolbox of blueprints and advanced techniques for building AI business applications *How to design and deploy AI applications that meet today’s business needs *A workflow from first design stages to practical code solutions in your next AI projects *Solutions for AI projects that involve social media analytics and recommendation engines *Practical projects and techniques for sentiment analysis and helpful chatbots *A blueprint for AI projects that recommend products based on customer purchasing habits *How to prepare yourself for the next decade of AI and machine learning advancements Who this book is for Programming AI Business Applications provides an introduction to AI with real-world examples. This book can be read and understood by programmers and students without requiring previous AI experience. The projects in this book make use of Java and Python and several popular and state-of-the-art opensource AI libraries.

Get into the world of smart data security using machine learning algorithms and Python libraries Key Features *Learn machine learning algorithms and cybersecurity fundamentals *Automate your daily workflow by applying use cases to many facets of security *Implement smart machine learning solutions to detect various cybersecurity problems Book Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learn *Use machine learning algorithms with complex datasets to implement cybersecurity concepts *Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems *Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA *Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes *Use TensorFlow in the cybersecurity domain and implement real-world examples *Learn how machine learning and Python can be used in complex cyber issues Who this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

Over 100 recipes to get up and running with the modern Linux administration ecosystem Key Features *Understand and implement the core system administration tasks in Linux *Discover tools and techniques to troubleshoot your Linux system *Maintain a healthy system with good security and backup practices Book Description Linux is one of the most widely used operating systems among system administrators,and even modern application and server development is heavily reliant on the Linux platform. The Linux Administration Cookbook is your go-to guide to get started on your Linux journey. It will help you understand what that strange little server is doing in the corner of your office, what the mysterious virtual machine languishing in Azure is crunching through, what that circuit-board-like thing is doing under your office TV, and why the LEDs on it are blinking rapidly. This book will get you started with administering Linux, giving you the knowledge and tools you need to troubleshoot day-to-day problems, ranging from a Raspberry Pi to a server in Azure, while giving you a good understanding of the fundamentals of how GNU/Linux works. Through the course of the book, you’ll install and configure a system, while the author regales you with errors and anecdotes from his vast experience as a data center hardware engineer, systems administrator, and DevOps consultant. By the end of the book, you will have gained practical knowledge of Linux, which will serve as a bedrock for learning Linux administration and aid you in your Linux journey. What you will learn *Install and manage a Linux server, both locally and in the cloud *Understand how to perform administration across all Linux distros *Work through evolving concepts such as IaaS versus PaaS, containers, and automation *Explore security and configuration best practices *Troubleshoot your system if something goes wrong *Discover and mitigate hardware issues, such as faulty memory and failing drives Who this book is for If you are a system engineer or system administrator with basic experience of working with Linux, this book is for you.

Design and implement professional level programs by exploring modern data structures and algorithms in Rust. Key Features * Use data structures such as arrays, stacks, trees, lists and graphs with real-world examples * Learn the functional and reactive implementations of the traditional data structures * Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Book Description Rust has come a long way and is now utilized in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not forgetting its importance in systems' programming. This book will be your guide as it takes you through implementing classic data structures and algorithms in Rust, helping you to get up and running as a confident Rust programmer. The book begins with an introduction to Rust data structures and algorithms, while also covering essential language constructs. You will learn how to store data using linked lists, arrays, stacks, and queues. You will also learn how to implement sorting and searching algorithms. You will learn how to attain high performance by implementing algorithms to string data types and implement hash structures in algorithm design. The book will examine algorithm analysis, including Brute Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, and Backtracking. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. What you will learn * Design and implement complex data structures in Rust * Analyze, implement, and improve searching and sorting algorithms in Rust * Create and use well-tested and reusable components with Rust * Understand the basics of multithreaded programming and advanced algorithm design * Become familiar with application profiling based on benchmarking and testing * Explore the borrowing complexity of implementing algorithms Who this book is for This book is for developers seeking to use Rust solutions in a practical/professional setting; who wants to learn essential Data Structures and Algorithms in Rust. It is for developers with basic Rust language knowledge, some experience in other programming languages is required.

Create highly efficient design patterns for scalability, redundancy, and high availability in the AWS Cloud Key Features * Build highly robust systems using the cloud infrastructure * Make web applications resilient against scheduled and accidental downtime * Explore and apply Amazon-provided services in unique ways to solve common design problems Book Description Whether you're just getting your feet wet in cloud infrastructure or already creating complex systems, this book will guide you through using the patterns to fit your system needs. Starting with patterns that cover basic processes such as source control and infrastructure-as-code, the book goes on to introduce cloud security practices. You'll then cover patterns of availability and scalability and get acquainted with the ephemeral nature of cloud environments. You'll also explore advanced DevOps patterns in operations and maintenance, before focusing on virtualization patterns such as containerization and serverless computing. In the final leg of your journey, this book will delve into data persistence and visualization patterns. You'll get to grips with architectures for processing static and dynamic data, as well as practices for managing streaming data. By the end of this book, you will be able to design applications that are tolerant of underlying hardware failures, resilient against an unexpected influx of data, and easy to manage and replicate. What you will learn * Implement scaling policies on schedules, influxes in traffic, and deep health checks * Make complete use of highly available and redundant storage * Design content delivery networks to improve user experience * Optimize databases through caching and sharding * Apply patterns to solve common problems * Implement repeatable processes for deploying systems Who this book is for If you’re an architect, solution provider, or DevOps community member looking to implement repeatable patterns for deploying and maintaining services in the Amazon cloud infrastructure, this book is for you. You’ll need prior experience of using AWS understand key concepts covered in the book, as it focuses on the patterns rather than the basics of using AWS.

Unleash the benefits of VMware vSphere 6.7 to provide a powerful, flexible and secure digital infrastructure Key Features * Deep dive into areas like management, security, scalability, availability and more with vSphere 6.7 * Design, deploy and manage VMware vSphere virtual datacenters * Implement monitoring and security of VMware workloads with ease Book Description vSphere 6.7 is the latest release of VMware’s industry-leading, virtual cloud platform. It allows organisations to move to hybrid cloud computing by enabling them to run, manage, connect and secure applications in a common operating environment. This up-to-date, 2nd edition provides complete coverage of vSphere 6.7. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin with an overview of the products, solutions and features of the vSphere 6.7 suite. You’ll learn how to design and plan a virtual infrastructure and look at the workflow and installation of components. You'll gain insight into best practice configuration, management and security. By the end the book you'll be able to build your own VMware vSphere lab that can run even the most demanding of workloads. What you will learn * Explore the immense functionality of vSphere 6.7 * Design, manage and administer a virtualization environment * Get tips for the VCP6-DCV and VCIX6-DCV exams * Understand how to implement different migration techniques across different environments * Explore vSphere 6.7s powerful capabilities for patching, upgrading and managing the configuration of virtual environments. * Understand core vSphere components * Master resource management, disaster recovery, troubleshooting, monitoring and security Who this book is for This book is for Administrators, Infrastructure Engineers, Architects, and Consultants with basic knowledge of VMware vSphere.

Build customized solutions for your business using the latest features of Odoo 12 and Python Key Features * Get up to speed with Odoo 12 to create custom and reusable modules * Interconnect your application with other systems by implementing web APIs * Understand the mechanisms powering the Odoo framework and Odoo.sh to build robust enterprises Book Description Odoo is a powerful framework known for rapid application development. Its latest release, Odoo 12, introduces tons of new features. With this book, you’ll learn how to develop powerful Odoo applications from scratch, using all the latest features. This Odoo cookbook starts by covering Odoo installation and deployment on the server. Next, you’ll explore the Odoo framework with real-world examples. You’ll create a new Odoo module from the ground up and progress to advanced framework concepts. You’ll also learn how to modify existing applications, including Point of Sale (POS). This book is not just limited to backend development; the advanced JavaScript recipes for creating new views and widgets will help you build beautiful UI elements. As you move forward, you’ll gain insights into website development and become a quality Odoo developer by studying performance optimization, debugging, and automated tests. Finally, you’ll learn the latest concepts like multi-website, In-App Purchasing (IAP), Odoo.sh, and IoT Box. By the end of the book, you’ll have all the knowledge you need to build powerful Odoo applications. The development best practices used in this book will undoubtedly come handy when you are working with the Odoo framework. What you will learn * Develop a module in the Odoo framework and modify the existing flow of any application * Build dynamic websites with Odoo CMS * Create and modify backend JavaScript components in Odoo and POS * Connect and access any object in Odoo via Remote Procedure Calls (RPC) * Illustrate the different tools available in Odoo to implement business processes for your records * Implement in-app purchase services * Manage, deploy, and test an Odoo instance with the PaaS Odoo.sh * Configure IoT Box to add and upgrade Point of Sale (POS) hardware Who this book is for If you're a Python developer who wants to develop highly efficient business applications with the latest Odoo framework, or if you just want a solution guide for all your Odoo development issues, this book is for you. Some JavaScript programming and web development experience is necessary to get the most out of this book.

Understand the constructs of the Python programming language and use them to build data science projects Key Features * Learn the basics of developing applications with Python and deploy your first data application * Take your first steps in Python programming by understanding and using data structures, variables, and loops * Delve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in Python Book Description Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards. What you will learn * Code in Python using Jupyter and VS Code * Explore the basics of coding – loops, variables, functions, and classes * Deploy continuous integration with Git, Bash, and DVC * Get to grips with Pandas, NumPy, and scikit-learn * Perform data visualization with Matplotlib, Altair, and Datashader * Create a package out of your code using poetry and test it with PyTest * Make your machine learning model accessible to anyone with the web API Who this book is for If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.

Explore the blockchain-based decentralized platform and understand how Ethereum works with Dapps examples Key Features * Explore the Ethereum ecosystem and understand the latest research on the platform * Build decentralized apps (Dapps) using smart contracts and Ethereum with the help of practical examples * Learn to make your decentralized applications fast and highly secure Book Description Ethereum is a blockchain-based, decentralized computing platform that allows running smart contracts. This book provides a basic overview of how Ethereum works, its ecosystem, mining process, and the consensus mechanism. It also demonstrates a step-by-step approach for building decentralized applications. This book begins with the very basics of Blockchain technology. Then it dives deep into the Ethereum architecture, framework and tools in its ecosystem. It also provides you an overview of ongoing research on Ethereum, for example, Layer 1 and 2 scaling solution, Stablecoin, ICO/STO/IEO, etc. Next, it explains Solidity language in detail, and provides step-by-step instructions for designing, developing, testing, deploying, and monitoring decentralized applications. In addition, you’ll learn how to use Truffle, Remix, Infura, Metamask, and many other Ethereum technologies. It’ll also help you develop your own cryptocurrency by creating ERC20, and ERC721 smart contracts from scratch. Finally, we explain private blockchains, and you learn how to interact with smart contracts through wallets. What you will learn * Understand the concepts of blockchain and cryptocurrency * Master Ethereum development tools such as Truffle, Remix IDE and Infura * Delve into smart contract development * Develop DApps frontend using Node.js, React.js, and Web3js API * Learn Etherscan and other tools to secure and monitor smart contracts * Develop and debug smart contracts by working with Remix * Apply Truffle suite to compile, migrate, and unit test smart contracts * Explore smart contracts such as ERC20 token and decentralized digital market Who this book is for This book is for all developers and architects who want to explore Ethereum blockchain fundamentals and get started with building real-world decentralized applications. Knowledge of an object-oriented programming language such as JavaScript will be useful but not mandatory.

Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features * Explore popular machine learning and deep learning services with their underlying algorithms * Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services * Design robust architectures to enable experimentation, extensibility, and maintainability of AI apps Book Description From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you’ll work through hands-on exercises and learn to use these services to solve real-world problems. You’ll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You’ll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you’ll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you’ll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learn * Gain useful insights into different machine and deep learning models * Build and deploy robust deep learning systems to production * Train machine and deep learning models with diverse infrastructure specifications * Scale AI apps without dealing with the complexity of managing the underlying infrastructure * Monitor and Manage AI experiments efficiently * Create AI apps using AWS pre-trained AI services Who this book is for This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.

Gain in-depth knowledge of TypeScript and the latest ECMAScript standards by building robust web applications across different domains Key Features * Apply the cutting-edge features of TypeScript 3.0 to build high-performance, maintainable applications * Learn through practical examples of using TypeScript with popular frameworks, such as Angular and React * Focus on building high-quality applications that are modular, scalable and adaptable Book Description With the demand for ever more complex websites, the need to write robust, standard-compliant JavaScript has never been greater. TypeScript is modern JavaScript with the support of a first-class type system, which makes it simpler to write complex web systems. With this book, you’ll explore core concepts and learn by building a series of websites and TypeScript apps. You’ll start with an introduction to TypeScript features that are often overlooked in other books, before moving on to creating a simple markdown parser. You’ll then explore React and get up to speed with creating a client-side contacts manager. Next, the book will help you discover the Angular framework and use the MEAN stack to create a photo gallery. Later sections will assist you in creating a GraphQL Angular Todo app and then writing a Socket.IO chatroom. The book will also lead you through developing your final Angular project which is a mapping app. As you progress, you’ll gain insights into React with Docker and microservices. You’ll even focus on how to build an image classification program with machine learning using TensorFlow. Finally, you’ll learn to combine TypeScript and C# to create an ASP.NET Core-based music library app. By the end of this book, you’ll be able to confidently use TypeScript 3.0 and different JavaScript frameworks to build high-quality apps. What you will learn * Discover how to use TypeScript to write code using common patterns * Get to grips with using popular frameworks and libraries with TypeScript * Leverage the power of both server and client using TypeScript * Learn how to apply exciting new paradigms such as GraphQL and TensorFlow * Use popular cloud-based authenticated services * Combine TypeScript with C# to create ASP.NET Core applications Who this book is for This book is for programmers and web developers who are familiar with TypeScript and want to put their knowledge to work by building real-world complex applications. Prior experience with any other web framework is not required.

An effective guide to designing, building, and deploying enterprise Java microservices with Eclipse MicroProfile Key Features * Create cloud-native microservices with ease using this detailed guide * Avoid vendor lock-in when implementing microservices using Eclipse MicroProfile * Discover why MicroProfile is a great specification for building microservices in multi-cloud environments Book Description Eclipse MicroProfile has gained momentum in the industry as a multi-vendor, interoperable, community-driven specification. It is a major disruptor that allows organizations with large investments in enterprise Java to move to microservices without spending a lot on retraining their workforce. This book is based on MicroProfile 2.2, however, it will guide you in running your applications in MicroProfile 3.0. You'll start by understanding why microservices are important in the digital economy and how MicroProfile addresses the need for enterprise Java microservices. You'll learn about the subprojects that make up a MicroProfile, its value proposition to organizations and developers, and its processes and governance. As you advance, the book takes you through the capabilities and code examples of MicroProfile’s subprojects - Config, Fault Tolerance, Health Check, JWT Propagation, Metrics, and OpenTracing. Finally, you’ll be guided in developing a conference application using Eclipse MicroProfile, and explore possible scenarios of what’s next in MicroProfile with Jakarta EE. By the end of this book, you'll have gained a clear understanding of Eclipse MicroProfile and its role in enterprise Java microservices. What you will learn * Understand why microservices are important in the digital economy * Analyze how MicroProfile addresses the need for enterprise Java microservices * Test and secure your applications with Eclipse MicroProfile * Get to grips with various MicroProfile capabilities such as OpenAPI and Typesafe REST Client * Explore reactive programming with MicroProfile Stream and Messaging candidate APIs * Discover and implement coding best practices using MicroProfile Who this book is for If you’re a Java developer who wants to create enterprise microservices, this book is for you. Familiarity with Java EE and the concept of microservices will help you get the most out of this book.

Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features *Master the art of real-time big data processing and machine learning *Explore a wide range of use-cases to analyze large data *Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: *Mastering Apache Spark 2.x by Romeo Kienzler *Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla *Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn *Get to grips with all the features of Apache Spark 2.x *Perform highly optimized real-time big data processing *Use ML and DL techniques with Spark MLlib and third-party tools *Analyze structured and unstructured data using SparkSQL and GraphX *Understand tuning, debugging, and monitoring of big data applications *Build scalable and fault-tolerant streaming applications *Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.

Leverage Elastic Stack’s machine learning features to gain valuable insight from your data Key Features * Combine machine learning with the analytic capabilities of Elastic Stack * Analyze large volumes of search data and gain actionable insight from them * Use external analytical tools with your Elastic Stack to improve its performance Book Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learn * Install the Elastic Stack to use machine learning features * Understand how Elastic machine learning is used to detect a variety of anomaly types * Apply effective anomaly detection to IT operations and security analytics * Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting * Combine your created jobs to correlate anomalies of different layers of infrastructure * Learn various tips and tricks to get the most out of Elastic machine learning Who this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.

A comprehensive certification guide that will help you prepare for the exam from day one of your preparation journey Key Features * Get well-versed with the nitty-gritty of Windows server 2016 * A practical guide towards installing, upgrading, and migrating to Windows Server 2016 * Enhance your skills with practice questions and mock tests Book Description MCSA: Windows Server 2016 is one of the most sought-after certifications for IT professionals which include working with Windows Server and performing administrative tasks around it. It targets Exam 70-740, Exam 70-741, Exam 70-742 and Exam 70-743 certifications and the demand for these certifications is increasingly high. This book will start with installation, upgrade and migration to Windows Server 2016, cover imaging and deployment and proceed with High Availability and Clustering. Then we will deep dive into fundamental concepts like Core networking, DNS, DHCP, Storage and proceed to Hyper-V, Network Access and Distributed File System. It will also explain Advanced Networking topics such as Software Defined Networking and High-Performance Networking. Furthermore, it will also cover advanced Identity topics in Windows Server 2016 such as Active Directory installation and configuration, Group Policy, Active Directory Certificate Services, and Active Directory Federation Services and Rights Management. Towards the end of this book, test questions and mock preparation items will help in preparing for the certifications with more ease. By the end of this book, you will be able to complete MCSA: Windows Server 2016 certification with more confidence. What you will learn * Prepare for exam 70-741 Networking with Windows Server 2016 * Learn to apply the technology to real-world examples * Learn core networking, name resolution and connectivity technologies in Windows Server 2016. * Get prepared for exam 70-742 Identity with Windows Server 2016 * Get ways to clear the exam 70-743 Upgrading Your Skills to MCSA: Windows Server 2016 * Practice critical skills and test your knowledge of the objectives of all three MCSA exams Who this book is for This book is targeted towards system administrators or windows server administrators who are interested in clearing the MCSA certification with ease.

Search, analyze, and manage data effectively with Elasticsearch 7 Key Features * Extend Elasticsearch functionalities and learn how to deploy on Elastic Cloud * Deploy and manage simple Elasticsearch nodes as well as complex cluster topologies * Explore the capabilities of Elasticsearch 7 with easy-to-follow recipes Book Description Elasticsearch is a Lucene-based distributed search server that allows users to index and search unstructured content with petabytes of data. With this book, you'll be guided through comprehensive recipes on what's new in Elasticsearch 7, and see how to create and run complex queries and analytics. Packed with recipes on performing index mapping, aggregation, and scripting using Elasticsearch, this fourth edition of Elasticsearch Cookbook will get you acquainted with numerous solutions and quick techniques for performing both every day and uncommon tasks such as deploying Elasticsearch nodes, integrating other tools to Elasticsearch, and creating different visualizations. You will install Kibana to monitor a cluster and also extend it using a variety of plugins. Finally, you will integrate your Java, Scala, Python, and big data applications such as Apache Spark and Pig with Elasticsearch, and create efficient data applications powered by enhanced functionalities and custom plugins. By the end of this book, you will have gained in-depth knowledge of implementing Elasticsearch architecture, and you'll be able to manage, search, and store data efficiently and effectively using Elasticsearch. What you will learn * Create an efficient architecture with Elasticsearch * Optimize search results by executing analytics aggregations * Build complex queries by managing indices and documents * Monitor the performance of your cluster and nodes * Design advanced mapping to take full control of index steps * Integrate Elasticsearch in Java, Scala, Python, and big data applications * Install Kibana to monitor clusters and extend it for plugins Who this book is for If you’re a software engineer, big data infrastructure engineer, or Elasticsearch developer, you'll find this book useful. This Elasticsearch book will also help data professionals working in the e-commerce and FMCG industry who use Elastic for metrics evaluation and search analytics to get deeper insights for better business decisions. Prior experience with Elasticsearch will help you get the most out of this book.

Improve your Delphi programming skills by building robust applications for Android, iOS, and Windows platform Key Features * Build responsive user interfaces (UIs) for desktop and mobile with FireMonkey * Implement a microservices architecture using the Rapid Application Development(RAD) server * Create clones of popular applications like Instagram and Facebook using Delphi 10.3 Book Description Delphi is a cross-platform programming language and software development kit that supports rapid application development for Microsoft Windows, Apple Mac OS X, Android, and iOS. With the help of seven practical projects, this book will guide you through the best practices, Delphi Run-Time Library (RTL) resources, and design patterns. Whether you use the Visual Component Library (VCL) or FireMonkey (FMX) framework, these design patterns will be implemented in the same way in Delphi, using Object Pascal. In the first few chapters, you will explore advanced features that will help you build rich applications using the same code base for both mobile and desktop projects. In addition to this, you’ll learn how to implement microservice architecture in Delphi. As you get familiar with the various aspects of Delphi, you will no longer need to maintain source code for similar projects, program business rules on screens, or fill your forms with data access components. By the end of this book, you will have gained an understanding of the principles of clean code and become proficient in building robust and scalable applications in Delphi. What you will learn * Get to grips with the advanced features of RTL * Understand how to deal with the paradigm change between multiplatform projects * Build rich interfaces with Google's Material Design features * Understand how to implement design patterns in Delphi * Turn a mobile device into a remote controller with app tethering technology * Build a multi-database system using VCL Who this book is for This book is for developers, programmers, and IT professionals who want to learn the best market practices by implementing practical projects. Prior knowledge of the Delphi language is a must.

Implement TDD for your React applications using Jest, React Router, Redux, and GraphQL/Relay. Learn BDD and end-to-end acceptance testing with CucumberJS and Puppeteer. Key Features * Learn the TDD process using the React framework * Build complex, real-world applications with a pragmatic approach to TDD * Use Cucumber for acceptance and BDD testing, bringing TDD to the wider team Book Description Many programmers are aware of TDD but struggle to apply it beyond basic examples. This book teaches how to build complex, real-world applications using Test-Driven Development (TDD). It takes a first principles approach to the TDD process using plain Jest and includes test-driving the integration of libraries including React Router, Redux, and Relay (GraphQL). Readers will practice systematic refactoring while building out their own test framework, gaining a deep understanding of TDD tools and techniques. They will learn how to test-drive features such as client- and server-side form validation, data filtering and searching, navigation and user workflow, undo/redo, animation, LocalStorage access, WebSocket communication, and querying GraphQL endpoints. The book covers refactoring codebases to use the React Router and Redux libraries. via TDD. Redux is explored in depth, with reducers, middleware, sagas, and connected React components. The book also covers acceptance testing using Cucumber and Puppeteer. The book is fully up to date with React 16.9 and has in-depth coverage of hooks and the ‘act’ test helper. What you will learn * Build test-driven applications using React 16.9+ and Jest * Build complete web applications using a variety of HTML input elements * Understand the different types of test double and when to apply them * Test-drive the Integration of libraries such as React Router, Redux, and Relay (GraphQL) * Learn when to be pragmatic and how to apply TDD shortcuts * Test-drive interaction with browser APIs including fetch and WebSockets * Use Cucumber.js and Puppeteer to build BDD-style acceptance tests for your applications * Build and test async Redux code using redux-saga and expect-redux Who this book is for The target audience for this book is JavaScript developers who are looking to implement test-driven and behavior-driven approaches for their React applications.

Learn the intricacies of managing Azure AD, Azure AD Connect as well as Active Directory for administration on cloud and Windows Server 2019 Key Features * Expert solutions for the federation, certificates, security, and monitoring with Active Directory * Explore Azure AD and AD Connect for effective administration on cloud * Automate security tasks using Active Directory and PowerShell Book Description Active Directory is an administration system for Windows administrators to automate network, security and access management tasks in the Windows infrastructure. This book starts off with a detailed focus on forests, domains, trusts, schemas and partitions. Next, you learn how to manage domain controllers, organizational units and the default containers. Going forward, you deep dive into managing Active Directory sites as well as identifying and solving replication problems. The next set of chapters covers the different components of Active Directory and discusses the management of users, groups and computers. You also go through recipes that help you manage your Active Directory domains, manage user and groups objects and computer accounts, expiring group memberships and group Managed Service Accounts with PowerShell. You learn how to work with Group Policy and how to get the most out of it. The last set of chapters covers federation, security and monitoring. You will also learn about Azure Active Directory and how to integrate on-premises Active Directory with Azure AD. You learn how Azure AD Connect synchronization works, which will help you manage Azure AD. By the end of the book, you have learned in detail about Active Directory and Azure AD, too. What you will learn * Manage new Active Directory features, such as the Recycle Bin, group Managed Service Accounts, and fine-grained password policies * Work with Active Directory from the command line and use Windows PowerShell to automate tasks * Create and remove forests, domains, and trusts * Create groups, modify group scope and type, and manage memberships * Delegate control, view and modify permissions * Optimize Active Directory and Azure AD in terms of security Who this book is for This book will cater to administrators of existing Active Directory Domain Services environments and/or Azure AD tenants, looking for guidance to optimize their day-to-day effectiveness. Basic networking and Windows Server Operating System knowledge would come in handy.

Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key Features * Build machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlow * Learn model optimization, and understand how to scale your models using simple and secure APIs * Develop, train, tune and deploy neural network models to accelerate model performance in the cloud Book Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learn * Manage AI workflows by using AWS cloud to deploy services that feed smart data products * Use SageMaker services to create recommendation models * Scale model training and deployment using Apache Spark on EMR * Understand how to cluster big data through EMR and seamlessly integrate it with SageMaker * Build deep learning models on AWS using TensorFlow and deploy them as services * Enhance your apps by combining Apache Spark and Amazon SageMaker Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Explore supervised machine learning with R by studying popular real-world uses cases such as object detection in driverless cars, customer churn, and default prediction Key Features * Study supervised learning algorithms by using real-world datasets * Fine tune optimal parameters with hyperparameter optimization * Select the best algorithm using the model evaluation framework Book Description R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms. By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. What you will learn * Develop analytical thinking to precisely identify a business problem * Wrangle data with dplyr, tidyr, and reshape2 * Visualize data with ggplot2 * Validate your supervised machine learning model using k-fold * Optimize hyperparameters with grid and random search, and Bayesian optimization * Deploy your model on Amazon Web Services (AWS) Lambda with plumber * Improve your model’s performance with feature selection and dimensionality reduction Who this book is for This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book.