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Home > Computing and Information Technology > Computer science > Artificial intelligence > Machine learning > Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch

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


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About the Book

Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you're looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you. Using Spark as your main data processing platform, you'll discover several open source technologies designed and built for enriching Spark's ML capabilities.Scaling Machine Learning with Spark examines various technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLFlow, TensorFlow, PyTorch, and Petastorm. This book shows you when to use each technology and why. If you're a data scientist working with machine learning, you'll learn how to:Build practical distributed machine learning workflows, including feature engineering and data formatsExtend deep learning functionalities beyond Spark by bridging into distributed TensorFlow and PyTorchManage your machine learning experiment lifecycle with MLFlowUse Petastorm as a storage layer for bridging data from Spark into TensorFlow and PyTorchUse machine learning terminology to understand distribution strategies

About the Author :
As Vice President of Developer Experience at Treeverse, Adi Polak shapes the future of data & ML technologies for hands-on builders. She also contributes to the lakeFS open-source, a git-like interface for object stores. In her work, Adi brings her vast industry research and engineering experience to bear in educating and helping teams design, architect, and build cost-effective data systems and machine learning pipelines that emphasize scalability, expertise, and business goals. Adi is a frequent worldwide presenter and the author of O'Reilly's upcoming book, "Machine Learning With Apache Spark." She is continually an invited member of multiple program committees and advisor for conferences like Data & AI Summit, Scale by the Bay, and others. Previously, Adi was a senior manager for Azure at Microsoft, where she focused on building advanced analytics systems and modern architectures. When Adi isn't building data pipelines or thinking up new software architecture, you can find her on the local cultural scene or at the beach.


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Product Details
  • ISBN-13: 9781098106829
  • Publisher: O'Reilly Media
  • Publisher Imprint: O'Reilly Media
  • Height: 233 mm
  • No of Pages: 400
  • Returnable: 00
  • Sub Title: Distributed ML with MLlib, TensorFlow, and PyTorch
  • ISBN-10: 1098106822
  • Publisher Date: 21 Mar 2023
  • Binding: Paperback
  • Language: English
  • Returnable: 00
  • Returnable: 00
  • Width: 178 mm


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