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Are you tired of manually deploying and managing your infrastructure and looking for ways to streamline your deployments, introduce consistency and collaboration, and save time? If so, then Learn Ansible is for you. Written by a DevOps practitioner and system administrator with 30+ years of experience, this book will teach you how to automate repetitive tasks and effortlessly manage several resources from a single code base.From installing Ansible and writing your first playbook to deploying multi-tier applications across different cloud platforms, this book will take you on an exciting learning journey. By learning the art of defining highly available cloud infrastructure using code, you’ll find it easy to distribute configurations alongside your application. You’ll explore Ansible Galaxy, learn about community-contributed Ansible roles, and discover how to create and share your own roles. Later, the book delves into the capabilities of Ansible AWX and integrating Ansible with your CI/CD pipelines, using Azure DevOps and GitHub Actions. With real-world examples and hands-on tutorials, you’ll build a solid foundation to tackle any automation project.By the end of this book, you'll be able to confidently implement Ansible in your environment and day-to-day workflows, taking your deployments to the next level.
David Flores, Christian Adell, Josh VanDeraa, Eric Chou, ...
As modern IT services and software architectures such as microservices rely increasingly on network performance, the relevance of networks has never been greater. Network observability has emerged as a critical evolution of traditional monitoring, providing the deep visibility needed to manage today’s complex, dynamic environments. In Modern Network Observability, authors David Flores, Christian Adell, and Josh VanDeraa share their extensive experience to guide you through building and deploying a flexible observability stack using open-source tools.This book begins by addressing the limitations of monolithic monitoring solutions, showing you how to transform them into a composable, flexible observability stack. Through practical implementations, you’ll learn how to collect, normalize, and analyze network data from diverse sources, build intuitive dashboards, and set up actionable alerts that help you stay ahead of potential issues. Later, you’ll cover advanced topics, such as integrating observability data into your network automation strategy, ensuring your network operations align with business objectives.By the end of this book, you'll be able to proactively manage your network, minimize downtime, and ensure resilient, efficient, and future-proof operations.
Over the past 10 years, deep learning has grown from being an academic research field to seeing wide-scale adoption across multiple industries. Deep learning models demonstrate excellent results on a wide range of practical tasks, underpinning emerging fields such as virtual assistants, autonomous driving, and robotics. In this book, you will learn about the practical aspects of designing, building, and optimizing deep learning workloads on Amazon SageMaker. The book also provides end-to-end implementation examples for popular deep-learning tasks, such as computer vision and natural language processing. You will begin by exploring key Amazon SageMaker capabilities in the context of deep learning. Then, you will explore in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker. You will learn how to train and serve deep learning models using popular open-source frameworks and understand the hardware and software options available for you on Amazon SageMaker. The book also covers various optimizations technique to improve the performance and cost characteristics of your deep learning workloads.By the end of this book, you will be fluent in the software and hardware aspects of running deep learning workloads using Amazon SageMaker.
Entity Framework is a highly recommended Object Relation Mapping tool used to build complex systems. In order to survive in this growing market, the knowledge of a framework that helps provide easy access to databases, that is, Entity Framework has become a necessity. This book will provide .NET developers with this knowledge and guide them through working efficiently with data using Entity Framework Core. You will start off by learning how to efficiently use Entity Framework in practical situations. You will gain a deep understanding of mapping properties and find out how to handle validation in Entity Framework. The book will then explain how to work with transactions and stored procedures along with improving Entity Framework using query libraries. Moving on, you will learn to improve complex query scenarios and implement transaction and concurrency control. You will then be taught to improve and develop Entity Framework in complex business scenarios. With the concluding chapter on performance and scalability, this book will get you ready to use Entity Framework proficiently.
C++ is a general-purpose programming language designed for efficiency, performance, and flexibility. Design patterns are commonly accepted solutions to well-recognized design problems. In essence, they are a library of reusable components, only for software architecture, and not for a concrete implementation. This book helps you focus on the design patterns that naturally adapt to your needs, and on the patterns that uniquely benefit from the features of C++. Armed with the knowledge of these patterns, you’ll spend less time searching for solutions to common problems and tackle challenges with the solutions developed from experience. You’ll also explore that design patterns are a concise and efficient way to communicate, as patterns are a familiar and recognizable solution to a specific problem and can convey a considerable amount of information with a single line of code. By the end of this book, you’ll have a deep understanding of how to use design patterns to write maintainable, robust, and reusable software.
Data Wrangling on AWS. Clean and organize complex data for analysis
Navnit Shukla, Sankar M, Sampat Palani
Data wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools.First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects.By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services.
Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards.After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
As the support for HTML5 within modern web browsers grows, the appeal of HTML5 development, especially for Flash developers, is at an all-time high. From 3D interactive multi-user content to high definition video streaming, HTML5 can deliver it all, to almost all modern web browsers, desktops, and mobile platforms. HTML5 for Flash Developers is a practical, targeted guide for developers with previous experience in developing applications in Flash who would like to add HTML5 development to their skill set. By understanding key aspects that make HTML5 development different from Flash you will be able to integrate your current skill set into the HTML5 development paradigm. Starting with identifying the key technologies that make up the HTML5 development stack and how they are utilized to create HTML5 content, HTML5 for Flash Developers will cover the traditional HTML5 application development flow chapter by chapter, while covering many other common tools that are regularly used in HTML5 today.In addition to understanding and writing dependency-free HTML5 code, you will also learn how to build your HTML5 applications on top of popular libraries and frameworks such as jQuery and CreateJS. This book covers how to prepare all of your content and media for integration into your HTML5 application using applications such as the Miro Video Converter or Adobe Photoshop. You will learn ways to not only continue to use Adobe Flash in your HTML5 application content preparation flow, but also how to port your existing Flash content right over to HTML5.Once you understand what it takes to build HTML5 content, you will finish up by learning how to debug, optimize, and prepare your content for release on the Web.
Jose Luis Latorre, Jose Luis Latorre Millas
With about ten years since its first release, Microsoft's .NET Framework 4.5 is one of the most solid development technologies to create casual, business, or enterprise applications. It has evolved into a very stable framework and solid framework for developing applications, with a solid core, called the CLR (Common Language Runtime) Microsoft .NET Framework 4.5 includes massive changes and enables modern application and UI development.Microsoft .Net Framework 4.5 Quickstart Cookbook aims to give you a run through the most exciting features of the latest version. You will experience all the flavors of .NET 4.5 hands on. The “How-to” recipes mix the right ingredients for a final taste of the most appetizing features and characteristics. The book is written in a way that enables you to dip in and out of the chapters.The book is full of practical code examples that are designed to clearly exemplify the different features and their applications in real-world development. All the chapters and recipes are progressive and based on the fresh features on .NET Framework 4.5.The book will begin by teaching you to build a modern UI application and improve it to make it Windows 8 Modern UI apps lifecycle model-compliant. You will create a portable library and throttle data source updating delays. Towards the end of the book, you will create you first Web API.
JetBrain’s PyCharm is the most popular Integrated Development Environment (IDE) used by the Python community thanks to its numerous features that facilitate faster, more accurate, and more productive programming practices. However, the abundance of options and customizations can make PyCharm seem quite intimidating.Hands-on Application Development with PyCharm starts with PyCharm’s installation and configuration process, and systematically takes you through a number of its powerful features that can greatly improve your productivity. You’ll explore code automation, version control, graphical debugging/testing, management of virtual environments, and much more. Finally, you’ll delve into specific PyCharm features that support web development and data science, two of the fastest growing applications in Python programming. These include the integration of the Django framework as well as the extensive support for IPython and Jupyter Notebook.By the end of this PyCharm book, you will have gained extensive knowledge of the tool and be able to implement its features and make the most of its support for your projects.
Learn WinUI 3.0. Leverage the power of WinUI, the future of native Windows application development
WinUI 3.0 takes a whole new approach to delivering Windows UI components and controls, and is able to deliver the same features on more than one version of Windows 10. Learn WinUI 3.0 is a comprehensive introduction to WinUI and Windows apps for anyone who is new to WinUI, Universal Windows Platform (UWP), and XAML applications.The book begins by helping you get to grips with the latest features in WinUI and shows you how XAML is used in UI development. You'll then set up a new Visual Studio environment and learn how to create a new UWP project. Next, you'll find out how to incorporate the Model-View-ViewModel (MVVM) pattern in a WinUI project and develop unit tests for ViewModel commands. Moving on, you'll cover the Windows Template Studio (WTS) new project wizard and WinUI libraries in a step-by-step way. As you advance, you'll discover how to leverage the Fluent Design system to create beautiful WinUI applications. You'll also explore the contents and capabilities of the Windows Community Toolkit and learn to create a new UWP user control. Toward the end, the book will teach you how to build, debug, unit test, deploy, and monitor apps in production.By the end of this book, you'll have learned how to build WinUI applications from scratch and modernize existing WPF and WinForms applications using WinUI controls.
The machine learning (ML) revolution has made our world unimaginable without its products and services. However, training ML models requires vast datasets, which entails a process plagued by high costs, errors, and privacy concerns associated with collecting and annotating real data. Synthetic data emerges as a promising solution to all these challenges.This book is designed to bridge theory and practice of using synthetic data, offering invaluable support for your ML journey. Synthetic Data for Machine Learning empowers you to tackle real data issues, enhance your ML models' performance, and gain a deep understanding of synthetic data generation. You’ll explore the strengths and weaknesses of various approaches, gaining practical knowledge with hands-on examples of modern methods, including Generative Adversarial Networks (GANs) and diffusion models. Additionally, you’ll uncover the secrets and best practices to harness the full potential of synthetic data.By the end of this book, you’ll have mastered synthetic data and positioned yourself as a market leader, ready for more advanced, cost-effective, and higher-quality data sources, setting you ahead of your peers in the next generation of ML.