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Large Scale Machine Learning with Python

You're reading from   Large Scale Machine Learning with Python Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

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Product type Paperback
Published in Aug 2016
Publisher Packt
ISBN-13 9781785887215
Length 420 pages
Edition 1st Edition
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Authors (3):
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Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
Bastiaan Sjardin Bastiaan Sjardin
Author Profile Icon Bastiaan Sjardin
Bastiaan Sjardin
Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
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Toc

Table of Contents (12) Chapters Close

Preface 1. First Steps to Scalability FREE CHAPTER 2. Scalable Learning in Scikit-learn 3. Fast SVM Implementations 4. Neural Networks and Deep Learning 5. Deep Learning with TensorFlow 6. Classification and Regression Trees at Scale 7. Unsupervised Learning at Scale 8. Distributed Environments – Hadoop and Spark 9. Practical Machine Learning with Spark A. Introduction to GPUs and Theano Index

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.

Note

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...

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