Data Science Workflows using Docker Containers

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Containerization technologies such as Docker enable software to run across various computing environments. Data Science requires auditable workflows where we can easily share and reproduce results. Docker is a useful tool that we can use to package libraries, code, and data into a single image. This talk will cover the basics of Docker; discuss how containers fit into Data Science workflows; and provide a quick-start guide that can be used as a template to create a shareable Docker image! Learn how to leverage the power of Docker without having to worry about the underlying details of the technology. Although this session is geared towards data scientists, the underlying concepts have many use cases (come find me after to discuss).

Aly Sivji

Founded in 2003, Chicago Python User Group is one of the world's most active programming language special interest groups with over 1,000 active members and many more prestigious alumni. Our main focus is the Python Programming Language.

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Why aren't all ML papers provide a Docker Container to replicate their code?
Thank you for the great and insightful presentation?

ze-speeches
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Came here looking for Bill & Ted Face the Music preview, and I was not disappointed. Just kidding, this is a GREAT overview, and not shying away from the important details.

dansplain
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Great Explanation, for beginners specially !

faiazrummankhan
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Great explanation of the dockerfile as well

kubercode
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the guy from dudes where's my car

nice presentation. I think too much alt tab csn be hard to follow and maybe more concrete use case - value proposition on why use docker

dougfoo
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nice intro to docker but I did not get one thing why would you use docker -a flag for STDIN STDOUT and STDERR?

keratishvili
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Great talk but anybody know where I can get more of this accent?

UpGado
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Dude! I know DataOps... And I know Kung Fu!! 😀

hypnotix_
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Some python ML libraries suck big time while compilation on windows. This is where docker will save u.

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