https://github.com/axsaucedo/scalable-data-science

Scalable Data Science: The state of DataOps / MLOps in 2018
https://github.com/axsaucedo/scalable-data-science

data dataops learning machine ml mlops scalable science

Last synced: about 7 hours ago
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Scalable Data Science: The state of DataOps / MLOps in 2018

• Host: GitHub
• URL: https://github.com/axsaucedo/scalable-data-science
• Owner: axsaucedo
• License: mit
• Created: 2018-08-29T00:49:53.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-09-30T14:04:42.000Z (about 6 years ago)
• Last Synced: 2024-07-30T17:59:46.473Z (3 months ago)
• Topics: data, dataops, learning, machine, ml, mlops, scalable, science
• Language: JavaScript
• Homepage: https://axsauze.github.io/scalable-data-science/#/
• Size: 21.2 MB
• Stars: 5
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Scaling Data Science Capabilities with Python

## [View Live Presentation](https://axsauze.github.io/scalable-data-science/#/)

Scalable Data Science in Python

## Running Presentation

You can also run the presentation on a local web server. Clone this repository and run the presentation like so:

```

npm install

grunt serve

```

The presentation can now be accessed on `localhost:8080`. Note that this web application is configured to bind to hostname `0.0.0.0`, which means that once the Grunt server is running, it will be accessible from external hosts as well (using the current host's public IP address).