Scaling Machine Learning with Spark (Fifth Early Release)

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