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Docker for Data Science - Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server (Paperback, 1st ed.)
Loot Price: R2,413
Discovery Miles 24 130
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Docker for Data Science - Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server (Paperback, 1st ed.)
Expected to ship within 10 - 15 working days
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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 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
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