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  • Mastering Machine Learning with Spark 2.x

    Harness the potential of machine learning, through spark

    Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorialKey Features\[\] Process and analyze big data in a distributed and scalable way\[\] Write sophisticated Spark pipelines that incorporate elaborate extraction\[\] Build and use regression models to predict flight delaysBook DescriptionThe purpose of machine learning is ... Read more

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  • MLOps with Ray

    Best Practices and Strategies for Adopting Machine Learning Operations

    Series series Professional and Applied Computing (R0)
    Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.The book delves into this engineering discipline's aspects and components and explores ... Read more

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  • Learning Ray

    Flexible Distributed Python for Machine Learning

    Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.Authors Max Pumperla, ... Read more

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  • Deep Learning and the Game of Go

    SummaryDeep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.Foreword by Thore Graepel, DeepMindPurchase of the print book includes a free eBook in PDF, Kindle, and ePub ... Read more

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  • Designing Machine Learning Systems

    An Iterative Process for Production-Ready Applications

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    Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments ... Read more

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  • Think Like a Programmer

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    The real challenge of programming isn't learning a language's syntax—it's learning to creatively solve problems so you can build something great. In this one-of-a-kind text, author V. Anton Spraul breaks down the ways that programmers solve problems and teaches you what other introductory books often ignore: how to Think Like a Programmer. Each chapter tackles a single programming concept, like ... Read more

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  • Data Fabric and Data Mesh Approaches with AI

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    Understand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance—all designed to deliver "data as a product" within hybrid cloud landscapes.This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive ... Read more

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  • Introduction to Machine Learning with Python

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    Software Architecture in the Age of Agility and DevOps

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  • Software Testing Foundations, 4th Edition

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    Software Testing Foundations, 4th Edition provides the information necessary to pass the Certified Tester-Foundations Level exam, version 2011, as defined by the ISTQB; uses a format designed for self-study; and defines technical terms according to the updated ISTQB glossary.Professional testing of software is an essential task that requires a profound knowledge of testing techniques. The ... Read more

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