Scaling Machine Learning with Spark

Distributed ML with MLlib, TensorFlow, and PyTorch

Author: Adi Polak

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    Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better.

    Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology.

    You will:

    • Explore machine learning, including distributed computing concepts and terminology
    • Manage the ML lifecycle with MLflow
    • Ingest data and perform basic preprocessing with Spark
    • Explore feature engineering, and use Spark to extract features
    • Train a model with MLlib and build a pipeline to reproduce it
    • Build a data system to combine the power of Spark with deep learning
    • Get a step-by-step example of working with distributed TensorFlow
    Adi Polak
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    Table of Contents (9789355429766_toc.pdf, 70 Kb) [Download]

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