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Creating Reproducible Data Science Workflows using Docker Containers
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Aly Sivji
Jupyter notebooks make it easy to create reproducible workflows that can be distributed across groups and organizations. This is a simple process provided that our end-users have access to the data along with a compatible Python environment. Learn how to use Docker to package a shareable image containing the libraries, code, and data required to reproduce every calculation.
PyOhio is a free (thanks sponsors!) annual conference for Python programmers in and around Ohio and the entire Midwest.
Jupyter notebooks make it easy to create reproducible workflows that can be distributed across groups and organizations. This is a simple process provided that our end-users have access to the data along with a compatible Python environment. Learn how to use Docker to package a shareable image containing the libraries, code, and data required to reproduce every calculation.
PyOhio is a free (thanks sponsors!) annual conference for Python programmers in and around Ohio and the entire Midwest.
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