CDN$ 109.87
Only 4 left in stock (more on the way).
Ships from and sold by Gift-wrap available.
Scaling up Machine Learni... has been added to your Cart
Have one to sell?
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See this image

Scaling up Machine Learning: Parallel and Distributed Approaches Hardcover – Dec 30 2011

See all 3 formats and editions Hide other formats and editions
Amazon Price
New from Used from
Kindle Edition
"Please retry"
"Please retry"
CDN$ 109.87
CDN$ 88.92 CDN$ 103.36

Harry Potter and the Cursed Child
click to open popover

Special Offers and Product Promotions

  • You'll save an extra 5% on Books purchased from, now through July 29th. No code necessary, discount applied at checkout. Here's how (restrictions apply)

No Kindle device required. Download one of the Free Kindle apps to start reading Kindle books on your smartphone, tablet, and computer.
Getting the download link through email is temporarily not available. Please check back later.

  • Apple
  • Android
  • Windows Phone
  • Android

To get the free app, enter your mobile phone number.

Product Details

  • Hardcover: 492 pages
  • Publisher: Cambridge University Press (Dec 30 2011)
  • Language: English
  • ISBN-10: 0521192242
  • ISBN-13: 978-0521192248
  • Product Dimensions: 21.5 x 2.7 x 25.3 cm
  • Shipping Weight: 998 g
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #1,338,251 in Books (See Top 100 in Books)
  •  Would you like to update product info, give feedback on images, or tell us about a lower price?

  • See Complete Table of Contents

Product Description


"One of the landmark achievements of our time is the ability to extract value from large volumes of data. Engineering and algorithmic developments on this front have gelled substantially in recent years, and are quickly being reduced to practice in widely-available, reusable forms. This book provides a broad and timely snapshot of the state of developments in scalable machine learning, which should be of interest to anyone who wishes to understand and extend the state of the art in analyzing data."
Joseph M. Hellerstein, University of California, Berkeley

"This is a book that every machine learning practitioner should keep in their library."
Yoram Singer, Google Inc.

"This unique, timely book provides a 360 degrees view and understanding of both conceptual and practical issues that arise when implementing leading machine learning algorithms on a wide range of parallel and high-performance computing platforms. It will serve as an indispensable handbook for the practitioner of large-scale data analytics and a guide to dealing with BIG data and making sound choices for efficient applying learning algorithms to them. It can also serve as the basis for an attractive graduate course on Parallel/Distributed Machine Learning and Data Mining."
Joydeep Ghosh, University of Texas

"The contributions in this book run the gamut from frameworks for large-scale learning to parallel algorithms to applications, and contributors include many of the top people in this burgeoning subfield. Overall this book is an invaluable resource for anyone interested in the problem of learning from and working with big datasets."
William W. Cohen, Carnegie Mellon University

"... an excellent resource for researchers in the field."
J. Arul for Computing Reviews

Book Description

In many practical situations, it is impossible to run existing machine learning methods on a single computer, because either the data is too large, or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.

See all Product Description

Customer Reviews

There are no customer reviews yet on
5 star
4 star
3 star
2 star
1 star

Most Helpful Customer Reviews on (beta) HASH(0xa32d6c90) out of 5 stars 2 reviews
3 of 3 people found the following review helpful
HASH(0xa32f2690) out of 5 stars Dated collection of research material May 6 2015
By techuser - Published on
Format: Hardcover
This book reads like a collection of dated papers (which are not even recent as of today).
HASH(0xa2f3b504) out of 5 stars Four Stars April 19 2016
By Kindle Customer - Published on
Format: Kindle Edition Verified Purchase
Nice coverage of a number of applications.