eoinmurray92

Pretty awesome this can be done in one command. At Kyso we deploy a lot of data-science images to GCP, it can be tricky.

Is the API support for this coming?

Happy to answer questions on this!

Thanks for reporting that - looking into it.

Currently we allow you to share notebooks statically, i.e. they don't run.

We will be supporting runnable dashboards and notebooks soon.

We are working on this, 1 click deploys of data-science apps - do you have a way for me to contact you when we launch this (I'm eoin at kyso.io)

We are planning this as a next feature for Kyso - email me at "eoin [at] kyso.io" if you need this soon.

You can currently use https://github.com/jupyter/colaboratory But its not under heavy development anymore, only internally at Google.

Any example code of how to do this?

We actually used the Knowledge-repo at our previous startup, and we thought we could improve the user experience, and hence https://kyso.io - but we haven't strayed to far from the original idea of the knowlege-repo

It allows data-scientists to easily deploy in-house data-apps. And by using the Juypter notebook, you get the GUI for free as well and automatic support for many many ML and numerical libraries (assuming Python)

So for example if a sales team gather data, and there is a data-transform thats done all the time by hand, or feeding new data to an ML model, an internal app would save time, and Jupyter dashboards have the potential to make this really easy.

Is there a way to toggle them all at once?

If I understand that what your looking for is easily deployable jupyter-dashboards (https://github.com/jupyter/dashboards) where you can make kind of micro data science apps. That is exactly what we have in mind as our next step.

Thanks for your feedback, currently we host static renderings of the notebooks, so you can share them easily for other to read. And others can pull them down locally and extend them.

We are planning the fully runnable Jupyter environment in the future.

Hi sandGorgon,

Thanks for your reply - this is on our roadmap, do you need the dashboards (we're the code runs, but is no editable) or full development notebooks?

We are planning both in the future - email me at "eoin [at] kyso.io" if you need this sooner rather than later.

edit: spelling

We currently host static renderings of the notebooks, for reading and sharing, where you can pull them down to your local installation of Jupyter to extend them.

On our roadmap is for Kyso to work like the Google drive integration, where you can use a jupyter kernel (https://github.com/jupyter/jupyter-drive) hosted anyway but server the notebooks from Kyso.

And we are also planning fully runnable notebooks too, we do aim to be the easiest way to run your own jupyter server.

We host Jupyter notebooks online! We don't in anyway replace Jupyter.

So normally when you publish a notebook online - you'd use Github or http://nbviewer.jupyter.org/

Github doesn't allow the interactive graphs, and nbviewer wont actually host the notebooks for you (you need to link from somewhere else).

Let you just type `kyso create "new-study"` and `kyso push "new version"` and your notebook is live online

edit for spelling

Just got some feedback there that the command line app is only working for node.js v8.0 and above!

Will be releasing a node v6.0 compatible version in the next 2 days, and an node independent version very soon!!

Eoin the co-founder of Kyso here! Happy to answer any questions...

my email is eoin [at] kyso.io

We had a lot of run doing SS!

Launched https://kyso.io - Kyso is the fastest way to share data analysis to your team and the world.

Our pres: https://www.startupschool.org/presentations/411

So the publication gets shared, and if you think its a good paper you set the a tag? This is a pretty good idea - but do you make the reviewer identify known?

Also Stripe wrote an article about this too - https://stripe.com/blog/reproducible-research

tldr: they use jupyter notebooks, and use an internal system to execute them upon commit.

Not really, It depends on the temperature of the particular system, but if the crystals are above something like 15 Kelvin then the particles should have a decent amount of energy each.

TensorFlow is the machine learning codebase, but typically how do machine learning research teams manage their training sets, dataset metadata and collaboration on these large datasets?

Its mostly similar to github, in that its a central place for you to talk about your data (for github its code), and there is a very strong track of each team members actions.

similarities to git; 1) we are making a portable datastore where the entirety of a body of data (metadata, discussion, data itself) can be moved around in a plain text format. 2) versioning and tracking of all evolutions to the data.

differences; obviously the diff'ing since a lot of datasets are non-text, or are very large (which we can handle).

Hella yeah! our latest integration is python, and R (mostly being requested by bio-statisticians) and C (for running hardware in labs) are coming next

Eoin here, co-founder of Rinocloud.

Hope this tool is useful to some of you - let me know if you have any questions. Im hoping to get some feedback.

Also are all the salaries in USD? I know Europe might pay less in tech - but the bottom three paying locations being Sweden, Berlin and London seems strange to me. Perhaps those locations salaries are not being converted to USD...

Rinocloud https://rinocloud.com/ - makes it easy to handle all your research data. You can save data with metadata and integrate Rinocloud with your instruments so that all the data can be curated automatically.

(quick demo here https://www.youtube.com/watch?v=LFRPAAj1ijc)

In my experience, your point 1 is quite true, but not totally. I have seen that VC's are very open to companies which will eventually die without funding. But in that case, they are most interested in an elite team (this need not be previous experience - just some metric which makes you 'top 10' - whatever you define and justify that to be). So its either; make it sound like you have an elite team, or not need the money. or...

One of the best things I have seen to convince investors, assuming this is your first raise or seed round, is showing them 'interviews' with customers or potential customers - we have made that a central theme at Rinocloud - to do as much 20 minute Skype calls with current and potentials users - showing these to investors has been a massive success.

How to investors make money on unicorns if there is no liquidity event?

Since people would start to want to buy cars more frequently because of the constant increate in performance/features (which the article is saying will be faster than the current state of motor innovation), would the car manufacturers be able to offer lower-cost models that would be bought every 3/4 years.

They could recoup the money from cheaper cars from the faster purchase cycle.

Or else move to a leasing model (this is becoming more popular with people I know in the UK, Ireland).