Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server

by Joshua Cook

Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller.
It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. 

As a solution to this problem, Docker for Data Science  proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies―Python, Jupyter, Postgres―as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms.
What  You'll Learn 
Who This Book Is For
Data scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers

Genres: Programming

278 pages, Paperback

Published August 25, 2017

Reviews

[Guilherme · Rating 4 out of 5]

The book focuses on teaching a tool - Docker and a procedure to perform data science in a modular way. It does not necessarily focus on other tools used throughout the book, what I thought was a great compromise. It really resonated with me since I had a similar work flow, but in a non dockerized approach. Sometimes the examples are verbose, and I appreciated, sometimes not so. To me if a application/data science project were fully carried out with the framework proposed the book would be formidable but I do understand it would drive away from the book proposition in first place.

[Matt Heavner · Rating 4 out of 5]

Despite a few bad typos/grammar-os (mostly in the text), this is a great, broad look at using Docker w/ Jupyter notebooks including networking, cloud, multiple databases or data stores. Really good. I read through it all and will definitely return to work through multiple parts of it again.

[Om Lalchandani · Rating 5 out of 5]

I think this is a must read book for any data scientist. It is a prized possession for a data analyst and professional.