Overview
- Teaches Docker principles with practical examples
- Covers high-performance interactive computing with Jupyter
- Presents a unique development method geared toward interactive computing
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Table of contents (10 chapters)
Keywords
About this book
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.Â
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 scenesand 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 LearnÂ
- Master interactive development using the Jupyter platform
- Run and build Docker containers from scratch and from publicly available open-source images
- Write infrastructure as code using the docker-compose tool and its docker-compose.yml file type
- Deploy a multi-service data science application across a cloud-based system
Who This Book Is For
Data scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Docker for Data Science
Book Subtitle: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Authors: Joshua Cook
DOI: https://doi.org/10.1007/978-1-4842-3012-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Joshua Cook 2017
Softcover ISBN: 978-1-4842-3011-4Published: 25 August 2017
eBook ISBN: 978-1-4842-3012-1Published: 23 August 2017
Edition Number: 1
Number of Pages: XXI, 257
Number of Illustrations: 21 b/w illustrations, 76 illustrations in colour
Topics: Big Data, Artificial Intelligence, Open Source, Python