Scaling Machine Learning with Spark

Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch

eBook Details:

  • Paperback: 291 pages
  • Publisher: WOW! eBook (April 11, 2023)
  • Language: English
  • ISBN-10: 1098106822
  • ISBN-13: 978-1098106829

eBook Description:

Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch

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.

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
  • Use PyTorch to scale machine learning and its internal architecture

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.

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