Github.
Reproducibility and Provenance in Data Science
Tobacco Dock - London - 19th July 2018
Schedule
0900 - Registration and breakfast
1000 - Keynote - How I learned to stop worrying and love version control - Dr Stephen J Newhouse and Luke Marsden Full details
1040 - Effective computing for research reproducibility - Dr Laura Fortunato Full details
1120 - Morning Break
1140 - A crazy little thing called reproducible science - Dr Tania Allard Full details
1220 - Machine Learning in Production - A practical approach to continuous deployment of Machine Learning pipelines - Luca Palmieri, Machine Learning & Data Scientist and Christos Dimitroulas, Fullstack developer & DevOps - Headstart.io Full details
1300 - Lunch
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1400 - 1730 Version Control for your Model, Data and Environment (Workshop)
In the afternoon workshop you’ll follow a set of hands-on self-paced exercises with support from our facilitators. You will leave being able to use tools like Docker, Git and dotscience to ensure the provenance and reproducibility of your models, environments and data.
Although all levels are encouraged to join in, some familiarity with working on the command line will be advantageous. You will also be required to bring your own laptop.
1800 - Networking drinks
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Code of Conduct
Please note that by attending the conference you agree to the following code of conduct .
Get involved.
- Sign up for Dothub for free.
- Try it via Katacoda, or the hello dotmesh tutorial.
- Check it out on Github.
- Give us feedback on Slack or get in touch via email.
- Learn more about what a datadot is.
- Browse the tutorials here.
Try on Katacoda.