Docker and Kubernetes for Java Developers. Scale, deploy, and monitor multi-container applications

Jarosław Krochmalski

Imagine creating and testing Java EE applications on Apache Tomcat Server or Wildfly Application server in minutes along with deploying and managing Java applications swiftly. Sounds too good to be true? But you have a reason to cheer as such scenarios are only possible by leveraging Docker and Kubernetes.This book will start by introducing Docker and delve deep into its networking and persistent storage concepts. You will then proceed to learn how to refactor monolith application into separate services by building an application and then packaging it into Docker containers. Next, you will create an image containing Java Enterprise Application and later run it using Docker. Moving on, the book will focus on Kubernetes and its features and you will learn to deploy a Java application to Kubernetes using Maven and monitor a Java application in production. By the end of the book, you will get hands-on with some more advanced topics to further extend your knowledge about Docker and Kubernetes.

Machine Learning with Apache Spark Quick Start Guide. Uncover patterns, derive actionable insights, and learn from big data using MLlib

Jillur Quddus

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently.But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it?The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.

PySpark Cookbook. Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

Denny Lee, Tomasz Drabas

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.

Mastering Embedded Linux Programming. Unleash the full potential of Embedded Linux with Linux 4.9 and Yocto Project 2.2 (Morty) Updates - Second Edition

Chris Simmonds

Embedded Linux runs many of the devices we use every day, from smart TVs to WiFi routers, test equipment to industrial controllers - all of them have Linux at their heart. Linux is a core technology in the implementation of the inter-connected world of the Internet of Things.The comprehensive guide shows you the technologies and techniques required to build Linux into embedded systems. You will begin by learning about the fundamental elements that underpin all embedded Linux projects: the toolchain, the bootloader, the kernel, and the root filesystem. You’ll see how to create each of these elements from scratch, and how to automate the process using Buildroot and the Yocto Project. Moving on, you’ll find out how to implement an effective storage strategy for flash memory chips, and how to install updates to the device remotely once it is deployed. You’ll also get to know the key aspects of writing code for embedded Linux, such as how to access hardware from applications, the implications of writing multi-threaded code, and techniques to manage memory in an efficient way. The final chapters show you how to debug your code, both in applications and in the Linux kernel, and how to profile the system so that you can look out for performance bottlenecks.By the end of the book, you will have a complete overview of the steps required to create a successful embedded Linux system.

Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition

Giuseppe Ciaburro, Prateek Joshi

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.

Building RESTful Web Services with PHP 7. Lumen, Composer, API testing, Microservices, and more

Waheed ud din

REST is the most wide spread and effective standard to develop APIs for internet services. With the way PHP and its eco-system has modernized the way code is written by simplifying various operations, it is useful to develop RESTful APIs with PHP 7 and modern tools.This book explains in detail how to create your own RESTful API in PHP 7 that can be consumed by other users in your organization. Starting with a brief introduction to the fundamentals of REST architecture and the new features in PHP 7, you will learn to implement basic RESTful API endpoints using vanilla PHP. The book explains how to identify flaws in security and design and teach you how to tackle them. You will learn about composer, Lumen framework and how to make your RESTful API cleaner, secure and efficient. The book emphasizes on automated tests, teaches about different testing types and give a brief introduction to microservices which is the natural way forward. After reading this book, you will have a clear understanding of the REST architecture and you can build a web service from scratch.

C# and .NET Core Test Driven Development. Dive into TDD to create flexible, maintainable, and production-ready .NET Core applications

Ayobami Adewole

This book guides developers to create robust, production-ready C# 7 and .NET Core applications through the practice of test-driven development process. In C# and .NET Core Test-Driven Development, you will learn the different stages of the TDD life cycle, basics of TDD, best practices, and anti-patterns. It will teach you how to create an ASP.NET Core MVC sample application, write testable code with SOLID principles and set up a dependency injection for your sample application. Next, you will learn the xUnit testing framework and learn how to use its attributes and assertions. You’ll see how to create data-driven unit tests and mock dependencies in your code. You will understand the difference between running and debugging your tests on .NET Core on LINUX versus Windows and Visual Studio. As you move forward, you will be able to create a healthy continuous integration process for your sample application using GitHub, TeamCity, Cake, and Microsoft VSTS.By the end of this book, you will have learned how to write clean and robust code through the effective practice of TDD, set up CI build steps to test and build applications as well as how to package application for deployment on NuGet.

Mobile Artificial Intelligence Projects. Develop seven projects on your smartphone using artificial intelligence and deep learning techniques

Karthikeyan NG, Arun Padmanabhan, Matt R. Cole

We’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision.This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms.By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users.