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

Setting up the VM

Setting up a cluster is a long and difficult operation; senior big data engineers earn their (high) salaries not just downloading and executing a binary application, but skillfully and carefully adapting the cluster manager to the desired working environment. It's a tough and complex operation; it may take a long time and if results are below the expectations, the whole business (including data scientists and software developers) won't be able to be productive. Data engineers must know every small detail of the nodes, data, operations that will be carried out, and network before starting to build the cluster. The output is usually a balanced, adaptive, fast, and reliable cluster, which can be used for years by all the technical people in the company.


Is a cluster with a low number of very powerful nodes better than a cluster with many less powerful servers? The answer should be evaluated case-by-case, and it's highly dependent on the data, processing algorithms, number...