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
About the Authors
About the Reviewer

The Hadoop ecosystem

Apache Hadoop is a very popular software framework for distributed storage and distributed processing on a cluster. Its strengths are in the price (it's free), flexibility (it's open source, and although being written in Java, it can by used by other programming languages), scalability (it can handle clusters composed by thousands of nodes), and robustness (it was inspired by a published paper from Google and has been around since 2011), making it the de facto standard to handle and process big data. Moreover, lots of other projects from the Apache foundation extend its functionalities.


Logically, Hadoop is composed of two pieces: distributed storage (HDFS) and distributed processing (YARN and MapReduce). Although the code is very complex, the overall architecture is fairly easy to understand. A client can access both storage and processing through two dedicated modules; they are then in charge of distributing the job across all theorking nodes:

All the Hadoop...