I see this as an uphill battle for MS. Everything about Machine Learning is easy to do on Mac and Linux, so everything for Machine Learning is currently done on those OSes. You can use ML products on Windows right now, but its been so difficult that no one bothered to. When the last Surface Pro came out I looked into switching, after about an hour of research I decided "Nope, nope, nope that looks too painful for too little benefit".
OTOH, MS definitely needed to do this to hopefully win some people like me over, in order to continue to gain ground on Amazon in Cloud usage, IBM in ML software, and Apple in OS and hardware. I'm also intrigued by their marketplace, if I can use this as an app store to sell my services, I might be interested.
Best of luck to you MS, you have a challenge ahead of you in this space, but you seem to be firing on all cylinders lately!
Great usability, but it only integrates happily with other Microsoft cloud services. You can't read from BigQuery or Amazon RDS, for example - only the Microsoft equivalents. It's a deeply disappointing decision, and already rules it out for a use case in our firm.
Microsoft shouldn't forget how GM was driven to bankruptcy at least partially by its inhouse part sourcing policy. If there's a weak link in the Microsoft cloud offering, users will simply hold out for Amazon's equivalent platform, which is undoubtedly a matter of time.
You're a challenger in the arena, so don't act like an entrenched incumbent.
All signs point towards Microsoft opening up their platforms rather than locking people in. I expect that in this case, it's more to do with the overhead of piping data in to and out of their data centers (I.E. paying for bandwidth) as opposed to getting bandwidth for free by having the data colocated with their ML servers.
Fair call on MS, but this allows for free downloading of datasets from public URLs. Surely that's more expensive than downloading from the other cloud platforms, given that you've got a (probably?) higher likelihood of USA-based hosting when pulling from AWS/Google.
What's needed is for Microsoft to make Azure machine learning accessible to a wider audience, via visualization tools with the level of ease of use like Tableau.
Sweet! Thanks for bringing that forward. If you ever make an academic edition that students could play with, please let me know! http://linkedin.com/in/tekelsey
As far as creating a model and deploying it to production, I cannot think of anything simpler and more wonderful to use than bigml. Outputs the decision trees /randomforests (via python api) in actual textual code. Hook that up to a Flask API and you've got your predictive web service.
0xdata.com is another one for more r/python users, can output gradient boosting models to straight up java objects (although that feature is pricey last time I looked into it), which of course is java so it's going to be pretty fast.
Really feeling for everyone out there still using PMML...
I'd prefer them improving UI allowing such simple things in browser as root shell to Linux VM. Otherwise you can kill hour or two trying to reset password that ex-colleague forgot to pass you. Sometimes you can't do it at all, without contacting support .....
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OTOH, MS definitely needed to do this to hopefully win some people like me over, in order to continue to gain ground on Amazon in Cloud usage, IBM in ML software, and Apple in OS and hardware. I'm also intrigued by their marketplace, if I can use this as an app store to sell my services, I might be interested.
Best of luck to you MS, you have a challenge ahead of you in this space, but you seem to be firing on all cylinders lately!
This is much more about establishing Azure as an ML platform than Windows.
Microsoft shouldn't forget how GM was driven to bankruptcy at least partially by its inhouse part sourcing policy. If there's a weak link in the Microsoft cloud offering, users will simply hold out for Amazon's equivalent platform, which is undoubtedly a matter of time.
You're a challenger in the arena, so don't act like an entrenched incumbent.
As far as creating a model and deploying it to production, I cannot think of anything simpler and more wonderful to use than bigml. Outputs the decision trees /randomforests (via python api) in actual textual code. Hook that up to a Flask API and you've got your predictive web service.
0xdata.com is another one for more r/python users, can output gradient boosting models to straight up java objects (although that feature is pricey last time I looked into it), which of course is java so it's going to be pretty fast.
Really feeling for everyone out there still using PMML...
http://powerbi.com/
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