Learning Continuous Integration with Jenkins. An end-to-end guide to creating operational, secure, resilient, and cost-effective CI/CD processes - Third Edition

Nikhil Pathania

This updated edition of Learning Continuous Integration with Jenkins is your one-stop guide to implementing CI/CD with Jenkins, addressing crucial technologies such as cloud computing, containerization, Infrastructure as Code, and GitOps. Tailored to both beginners and seasoned developers, the book provides a practical path to mastering a production-grade, secure, resilient, and cost-effective CI/CD setup.Starting with a detailed introduction to the fundamental principles of CI, this book systematically takes you through setting up a CI environment using Jenkins and other pivotal DevOps tools within the CI/CD ecosystem. You’ll learn to write pipeline code with AI assistance and craft your own CI pipeline. With the help of hands-on tutorials, you’ll gain a profound understanding of the CI process and Jenkins’ robust capabilities. Additionally, the book teaches you how to expand your CI pipeline with automated testing and deployment, setting the stage for continuous deployment. To help you through the complete software delivery process, this book also covers methods to ensure that your CI/CD setup is maintainable across teams, secure, and performs optimally.By the end of the book, you’ll have become an expert in implementing and optimizing CI/CD setups across diverse teams.

Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition

Sinan Ozdemir

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

Microsoft Power Apps Cookbook. Build user-friendly apps, troubleshoot challenges, and navigate the evolving Power Apps landscape - Third Edition

Eickhel Mendoza

In the rapidly evolving world of low-code development, Microsoft Power Apps stands out as a powerful platform for building custom business solutions. Microsoft Power Apps Cookbook, 3rd Edition, is your hands-on guide to mastering this platform. Through a collection of step-by-step recipes, this updated edition helps you navigate the latest features, such as AI-powered Microsoft Copilot and custom UI elements while empowering you to build efficient and scalable apps.This book emphasizes practical solutions, guiding app makers through building everything from canvas apps to complex data integrations. You will learn how to streamline repetitive tasks using Robotic Process Automation (RPA) and explore how to create external-facing websites using Microsoft Power Pages while handling data management with Dataverse and extending app functionality with the Power Apps Component Framework.Whether you're extending your app's capabilities with custom components or integrating advanced AI features, Microsoft Power Apps Cookbook equips you with the knowledge and skills to take your app development to the next level.

Redis Stack for Application Modernization. Build real-time multi-model applications at any scale with Redis

Luigi Fugaro, Mirko Ortensi

In modern applications, efficiency in both operational and analytical aspects is paramount, demanding predictable performance across varied workloads. This book introduces you to Redis Stack, an extension of Redis and guides you through its broad data modeling capabilities. With practical examples of real-time queries and searches, you’ll explore Redis Stack’s new approach to providing a rich data modeling experience all within the same database server.You’ll learn how to model and search your data in the JSON and hash data types and work with features such as vector similarity search, which adds semantic search capabilities to your applications to search for similar texts, images, or audio files. The book also shows you how to use the probabilistic Bloom filters to efficiently resolve recurrent big data problems. As you uncover the strengths of Redis Stack as a data platform, you’ll explore use cases for managing database events and leveraging introduce stream processing features. Finally, you’ll see how Redis Stack seamlessly integrates into microservices architectures, completing the picture.By the end of this book, you’ll be equipped with best practices for administering and managing the server, ensuring scalability, high availability, data integrity, stored functions, and more.

Data Science with .NET and Polyglot Notebooks. Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel

Matt Eland

As the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft’s .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you’ll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you’ll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem.

LLVM Code Generation. A deep dive into compiler backend development

Quentin Colombet, Kristof Beyls

The LLVM infrastructure is a popular compiler ecosystem widely used in the tech industry and academia. This technology is crucial for both experienced and aspiring compiler developers looking to make an impact in the field. Written by Quentin Colombet, a veteran LLVM contributor and architect of the GlobalISel framework, this book provides a primer on the main aspects of LLVM, with an emphasis on its backend infrastructure; that is, everything needed to transform the intermediate representation (IR) produced by frontends like Clang into assembly code and object files.You’ll learn how to write an optimizing code generator for a toy backend in LLVM. The chapters will guide you step by step through building this backend while exploring key concepts, such as the ABI, cost model, and register allocation. You’ll also find out how to express these concepts using LLVM's existing infrastructure and how established backends address these challenges. Furthermore, the book features code snippets that demonstrate the actual APIs.By the end of this book, you’ll have gained a deeper understanding of LLVM. The concepts presented are expected to remain stable across different LLVM versions, making this book a reliable quick reference guide for understanding LLVM.

Mastering Windows 365. Deploy and Manage Cloud PCs and Windows 365 Link devices, Copilot with Intune, and Intune Suite - Second Edition

Christiaan Brinkhoff, Sandeep Patnaik, Morten Pedholt, Scott Manchester, ...

Windows 365 Cloud PC continues to evolve, integrating AI-driven management, enhanced security, and expanded capabilities to provide a seamless cloud-based Windows experience. This second edition builds on the foundation of the first, incorporating new content on Intune Suite, Copilot+ AI PCs, Windows App, and advanced security with Security Copilot to help IT professionals deploy, manage, and optimize Windows 365 Cloud PCs effectively.This edition expands beyond the basics, covering Intune Suite’s role in optimizing and securing deployments, new methods for application management and delivery, and insights into Windows 365 Link for hybrid cloud environments. You’ll also explore AI-powered administration with Security Copilot, providing intelligent security management and automation.Written by experts from the Windows 365 product team and a Microsoft MVP, this book provides practical guidance, best practices, and real-world insights to help you master modern Windows cloud management. Whether you’re working with Windows 365, Intune Suite, or AI-powered administration, this guide equips you with the latest tools and strategies to stay ahead in cloud computing.

Journey to Become a Google Cloud Machine Learning Engineer. Build the mind and hand of a Google Certified ML professional

Dr. Logan Song

This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer.The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional.The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together.The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate.