Simple data science collaboration & productivity on the web

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Fareed MosavatVP Product, RunKeeper
I've been thinking a lot about this problem - sharing and collaborating on iPython notebooks, tracking changes, keeping analytics code up-to-date, etc are all big pain in the ass problems. Curious to see how the product evolves.
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Systems & ops challenges dramatically reduce productivity of data science & engineering teams across the analytics lifecycle. ScienceBox eliminates a myriad of repetitive, time consuming and error prone tasks and enable data science teams to get straight to the data science. Would love to hear what people think!
Josh Hubball
Founder @Levelframes
@theglamp what kind of tasks are data science teams using this for?
Upvote (1)
Austin Ogilvie
Founder & CEO, Yhat, Inc.
@theglamp @jhubball @theglamp @jhubball TL;DR; productivity and collaboration. Eliminating IT overhead is a huge one. Data science teams need flexibility to customize hardware and scientific tools without waiting for a sys admin gatekeeping the corporate AWS account. Seems so simple but a big amount of time is wasted on hard-to-manage data science tools. Another one is collaboration. ScienceBox fills collaboration needs specific to data science (e.g. roles/permissions, shared scripts and IPython Notebooks, putting jobs and job scheduling into the hands of the data science team directly, and making it super easy to reproduce the results of another person on your team (or your own results last month for that matter).