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parsed(2023-04-11) - pubdate: 2023-04-11
pub date: 1681189200
today: 1685595600, pubdate > today = false

nyp: 0;

Scaling Machine Learning with Spark

Distributed ML with MLlib, TensorFlow, and PyTorch

April 11, 2023 | Trade paperback
ISBN: 9781098106829
Reader Reward Price: $100.76 info
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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
  • Use PyTorch to scale machine learning and its internal architecture

ISBN: 9781098106829
Format: Trade paperback
Pages: 291
Publisher: O'Reilly Media
Published: 2023-04-11

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