Scaling Machine Learning with Spark - Distributed ML with MLlib, TensorFlow, and PyTorch (Paperback)

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

General

Imprint:	O'Reilly Media
Country of origin:	United States
Release date:	February 2023
Authors:	Adi Polak
Dimensions:	233 x 178mm (L x W)
Format:	Paperback
Pages:	400
ISBN-13:	978-1-09-810682-9
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-09-810682-2
Barcode:	9781098106829

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!