<ul>
<li>Endorsed by all major vendors (Microsoft, Oracle, IBM, and SAP), SOA has quickly become the industry standard for building next-generation software; this practical guide shows readers how to achieve the many benefits of SOA</li> <li>Begins with a look at the architectural principles needed to create successful applications and then goes on to examine the process for designing services and SOA implementations</li> <li>Each stage of the design process has an accompanying chapter that walks readers through the details and provides helpful tips, techniques, and examples</li> <li>The author team of SOA practitioners also provides two unique, comprehensive, end-to-end case studies illustrating the architectural and design techniques presented in the book</li> </ul>

Applied SOA: Service-Oriented Architecture and Design Strategies

Computers & Technology

ROSEN, Michaël

Lublinsky, Boris

Smith, Kevin T.

Balcer, Marc J.

Web Development & Design

wiley

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators. In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You'll explore distributed processing (the pure Python implementation of serverless) and learn how to: <ul> <li>Implement stateful applications with Ray actors</li> <li>Build workflow management in Ray</li> <li>Use Ray as a unified system for batch and stream processing</li> <li>Apply advanced data processing with Ray</li> <li>Build microservices with Ray</li> <li>Implement reliable Ray applications</li> </ul>

Scaling Python with Ray: Adventures in Cloud and Serverless Patterns

Karau, Holden

Networking & Cloud Computing

O'Reilly Media

The go-to guidebook for deploying Big Data solutions with Hadoop Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth. With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them. <ul>
<li>The ultimate guide for developers, designers, and architects who need to build and deploy Hadoop applications</li> <li>Covers storing and processing data with various technologies, automating data processing, Hadoop security, and delivering real-time solutions</li> <li>Includes detailed, real-world examples and code-level guidelines</li> <li>Explains when, why, and how to use these tools effectively</li> <li>Written by a team of Hadoop experts in the programmer-to-programmer Wrox style</li> </ul>
Professional Hadoop Solutions is the reference enterprise architects and developers need to maximize the power of Hadoop.

Professional Hadoop Solutions

Yakubovich, Alexey

Wrox

<div> If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. <ul> <li>Understand Kubeflow's design, core components, and the problems it solves</li> <li>Understand the differences between Kubeflow on different cluster types</li> <li>Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark</li> <li>Keep your model up to date with Kubeflow Pipelines</li> <li>Understand how to capture model training metadata</li> <li>Explore how to extend Kubeflow with additional open source tools</li> <li>Use hyperparameter tuning for training</li> <li>Learn how to serve your model in production</li> </ul> </div>

Kubeflow for Machine Learning: From Lab to Production

Grant, Trevor

Liu, Richard

Filonenko, Ilan

Computer Science

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