- Paperback: 257 pages
- Publisher: Apress; 1st ed. edition (19 December 2017)
- Language: English
- ISBN-10: 9781484230114
- ISBN-13: 978-1484230114
- ASIN: 1484230116
- Product Dimensions: 15.5 x 1.6 x 23.5 cm
- Average Customer Review: Be the first to review this item
Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server Paperback – Import, 19 Dec 2017
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From the Back Cover
- 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
About the Author
Joshua Cook is a mathematician. He writes code in Bash, C, and Python and has done pure and applied computational work in geo-spatial predictive modeling, quantum mechanics, semantic search, and artificial intelligence. He also has 10 years experience teaching mathematics at the secondary and post-secondary level. His research interests lie in high-performance computing, interactive computing, feature extraction, and reinforcement learning. He is always willing to discuss orthogonality or to explain why Fortran is the language of the future over a warm or cold beverage.
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Most helpful customer reviews on Amazon.com
While geared towards Data Scientists using Python and Sklearn, it was written by a developer, not a DevOps/Systems Engineer/SRE. This allows it to give practical examples that extend beyond the "Hello World" toy examples that you'll find in most tutorials and other books.
All in all an excellent book, and my first recommendation to anyone who is looking to learn Docker.