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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 formats Extend deep learning functionalities
beyond Spark by bridging into distributed TensorFlow and PyTorch
Manage your machine learning experiment lifecycle with MLFlow Use
Petastorm as a storage layer for bridging data from Spark into
TensorFlow and PyTorch Use machine learning terminology to
understand distribution strategies
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