Free PDF Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
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Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Free PDF Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
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From the Back Cover
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:Master interactive development using the Jupyter platformRun and build Docker containers from scratch and from publicly available open-source imagesWrite infrastructure as code using the docker-compose tool and its docker-compose.yml file typeDeploy a multi-service data science application across a cloud-based system
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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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Product details
Paperback: 257 pages
Publisher: Apress; 1st ed. edition (August 25, 2017)
Language: English
ISBN-10: 9781484230114
ISBN-13: 978-1484230114
ASIN: 1484230116
Product Dimensions:
6.1 x 0.6 x 9.2 inches
Shipping Weight: 14.4 ounces (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#593,342 in Books (See Top 100 in Books)
This book is hands down the best explanation of Docker I've ever read. As a Sr. Systems Engineer at multiple Fortune 500 companies this is my first recommendation for any developer looking to learn Docker.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.
This book is incredibly informative, clearly written, and full of examples and patterns that I use everyday now as a data scientist. I would recommend it to any professional in the field.
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