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

Streaming data from sources


Some data is really streaming through your computer when you have a generative process that transmits data, which you can process on the fly or just discard, but not recall afterward unless you have stored it away in some data archival repository somewhere. It is like dragging water from a flowing river—the river keeps on flowing but you can filter and process all the water as it goes. It's a completely different strategy from processing all the data at once, which is more like putting all the water in a dam (an analogy for working with all the data in-memory).

As an example of streaming, we could quote the data flow produced instant by instant by a sensor or, even more simply, a Twitter streamline of tweets. Generally, the main sources of data streams are as follows:

  • Environment sensors measuring temperature, pressure, and humidity

  • GPS tracking sensors recording the location (latitude/longitude)

  • Satellites recording image data

  • Surveillance videos and sound records...