Azure Jupyter Notebooks

I suppose it's publicly hosted which might be rare or totally new. TBH the most frustrating thing is that unlike github's notebook hosting you need to login just to view the content.

And for some reason the back button doesn't work once you encounter the login page. That's enough to make me want to stay away.

Actually, this is somewhat surprising. It's taking what many, MANY data scientists and analysts are actually using day-to-day and making it point-and-click to start in the cloud.

You can set these things up in AWS and GCE, but it requires a lot more work. Here is my top google hit for setting things up on AWS, from a relatively recent and maintained repository: https://gist.github.com/iamatypeofwalrus/5183133

Many data scientists just run their notebooks locally. Few have the luxury of Data Science Engineers on hand to help them through the complex world of provisioning and maintaining servers, even in this canned AWS world where spark clusters unfold themselves in EMR.

Azure is absolutely full of "here is a canned solution for _insert common engineer task with open source focus here_" buttons and it's really quite great as a result.

Also: Azure is doing a very good job of making it difficult to have a default-insecure instance. You don't make choices about encryption-at-rest, for example. That's forced on you. There is a lot more tooling around sharing accounts and giving users minimal ACLs rather than just doing the simple thing in AWS or GCE and giving out power user accounts.

It's simple stuff, of course, but this simple stuff is the simple stuff AWS has elected not to do, so...

I'm... confused what you're upset about. Are you upset about the leading "Chart?"

This is very, very easy stuff for functional programming. Most of the people I know in ML, talented ML folks doing data science within the org I work for to do exciting things... they actually aren't huge fans of the way Python does things. They're huge fans of the libraries Python has to speed up ML work. F# has a real lack there (surprisingly!).

But like, saying that you like:

    Chart.Line(data).WithTitle("smth").render()

as opposed to:

    Chart.Line(data) |> Chart.WithTitle("smdh")

seems to me like 6 of one or half a dozen of the other. Especially since when you're reusing any given part of the pipeline, the ML way is alot more terse.

And it's not like Python is devoid of hideous examples of poor language design. For example, self parameters. All we're told is some new age mysticism about how "explicit is better than implicit", but somehow that doesn't apply when we need higher order functions and we're using private function with lambda names to try and signal to readers that, "Sorry I needed 2 statements in my list comprehension and Python talks down to me like that guy from Timecube because of it." Why is explicitness valuable there but not on tuple construction?

And it's not like Python doesn't make you arbitrarily choose between List.function and list.method with no seeming decisions to make.

Which is not to say Python is especially bad. It is to say that you may have normalized Python so much that you forget all computer languages are somewhat arbitrary.

It is very odd-looking.

And it's really slow.

It took me 55 seconds to load the page (42 of which went into downloading a 6 MB large GIF). 40+ requests. And it doesn't seem like a slow Internet problem at all.

I think your impressions of Microsoft are out-of-date. We've been successfully using Azure resources through "Azure for Research" grants. My world, is the world of Linux VMs, Docker, and OSS and I find Azure to be a good fit for our research needs. (I never touch any Windows OS, if that's what you are worried about.)