Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes

Faisal Masood, Ross Brigoli

MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization.You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow.By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.

Electron Projects. Build over 9 cross-platform desktop applications from scratch

Denys Vuika

The Electron framework allows you to use modern web technologies to build applications that share the same code across all operating systems and platforms. This also helps designers to easily transition from the web to the desktop. Electron Projects guides you through building cross-platform Electron apps with modern web technologies and JavaScript frameworks such as Angular, React.js, and Vue.js.You’ll explore the process of configuring modern JavaScript frameworks and UI libraries, real-time analytics and automatic updates, and interactions with the operating system. You’ll get hands-on with building a basic Electron app, before moving on to implement a Markdown Editor. In addition to this, you’ll be able to experiment with major JavaScript frameworks such as Angular and Vue.js, discovering ways to integrate them with Electron apps for building cross-platform desktop apps. Later, you’ll learn to build a screenshot snipping tool, a mini-game, and a music player, while also gaining insights into analytics, bug tracking, and licensing. You’ll then get to grips with building a chat app, an eBook generator and finally a simple digital wallet app.By the end of this book, you’ll have experience in building a variety of projects and project templates that will help you to apply your knowledge when creating your own cross-platform applications.

Expert Python Programming. Become a master in Python by learning coding best practices and advanced programming concepts in Python 3.7 - Third Edition

Michał Jaworski, Tarek Ziadé

Python is a dynamic programming language that's used in a wide range of domains thanks to its simple yet powerful nature. Although writing Python code is easy, making it readable, reusable, and easy to maintain is challenging. Complete with best practices, useful tools, and standards implemented by professional Python developers, the third edition of Expert Python Programming will help you overcome this challenge.The book will start by taking you through the new features in Python 3.7. You'll then learn the advanced components of Python syntax, in addition to understanding how to apply concepts of various programming paradigms, including object-oriented programming, functional programming, and event-driven programming. This book will also guide you through learning the naming best practices, writing your own distributable Python packages, and getting up to speed with automated ways to deploy your software on remote servers. You’ll discover how to create useful Python extensions with C, C++, Cython, and CFFI. Furthermore, studying about code management tools, writing clear documentation, and exploring test-driven development will help you write clean code.By the end of the book, you will have become an expert in writing efficient and maintainable Python code.

Learning Continuous Integration with Jenkins

Nikhil Pathania

In past few years, Agile software development has seen tremendous growth across the world. There is huge demand for software delivery solutions that are fast yet flexible to frequent amendments. As a result, CI and continuous delivery methodologies are gaining popularity. Jenkins’ core functionality and flexibility allows it to fit in a variety of environments and can help streamline the development process for all stakeholders.This book starts off by explaining the concepts of CI and its significance in the Agile world with a whole chapter dedicated to it. Next, you’ll learn to configure and set up Jenkins. You’ll gain a foothold in implementing CI and continuous delivery methods. We dive into the various features offered by Jenkins one by one exploiting them for CI. After that, you’ll find out how to use the built-in pipeline feature of Jenkins. You’ll see how to integrate Jenkins with code analysis tools and test automation tools in order to achieve continuous delivery. Next, you’ll be introduced to continuous deployment and learn to achieve it using Jenkins.Through this book’s wealth of best practices and real-world tips, you'll discover how easy it is to implement a CI service with Jenkins.

Google Plus First Look: a tip-packed, comprehensive look at Google+. Get up and running with Google+ fast with this book and

Ralph Roberts

Big Data Analytics with R. Leverage R Programming to uncover hidden patterns in your Big Data

Simon Walkowiak

Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing.The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.

Learning Material Design. Master Material Design and create beautiful, animated interfaces for mobile and web applications

Kyle Mew

Google's Material Design language has taken the web development and design worlds by storm. Now available on many more platforms than Android, Material Design uses color, light, and movements to not only generate beautiful interfaces, but to provide intuitive navigation for the user.Learning Material Design will teach you the fundamental theories of Material Design using code samples to put these theories into practice.Focusing primarily on Android Studio, you’ll create mobile interfaces using the most widely used and powerful material components, such as sliding drawers and floating action buttons. Each section will introduce the relevant Java classes and APIs required to implement these components. With the rules regarding structure, layout, iconography, and typography covered, we then move into animation and transition, possibly Material Design's most powerful concept, allowing complex hierarchies to be displayed simply and stylishly.With all the basic technologies and concepts mastered, the book concludes by showing you how these skills can be applied to other platforms, in particular web apps, using the powerful Polymer library.

Hands-On Reinforcement Learning for Games. Implementing self-learning agents in games using artificial intelligence techniques

Micheal Lanham

With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python.Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games.By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications.