Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

From a standalone machine to a bunch of nodes


The amount of data stored in the world is increasing exponentially. Nowadays, for a data scientist, having to process a few Terabytes of data a day is not an unusual request. To make things more complex, usually data comes from many different heterogeneous systems and the expectation of business is to produce a model within a short time.

Handling big data, therefore, is not just a matter of size, it's actually a three-dimensional phenomenon. In fact, according to the 3V model, systems operating on big data can be classified using three (orthogonal) criteria:

  1. The first criterion is the velocity that the system archives to process the data. Although a few years ago, speed was indicating how quickly a system was able to process a batch; nowadays, velocity indicates whether a system can provide real-time outputs on streaming data.

  2. The second criterion is volume, that is, how much information is available to be processed. It can be expressed in number...