
Azure Machine Learning: A Brief Introduction - ntakasaki
https://projectbotticelli.com/knowledge/brief-introduction-to-microsoft-azure-ml?
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dk8996
I think there is a big craze with regard to ML. Most people just draw a black
box call it ML/Brain/"hire data scientist" without realizing that for most
nontrivial problems its not going to work like magic. Some of the things that
I see people underestimating is; a) how hard it is to make the magic black box
b) amount of data you need c) how clean the data needs to be d) at times, you
need lots of human annotated data -- cost and time to collect it e) how much
it's a art than a science. Moreover, people don't have a good understating of
the technical challenges and cost when you need to scale to n (features) * m
(customers) * p (products). Maybe Azure solves some of these challenges but
quickly looking at the pricing -- it seems like its a bit too costly.

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louisdorard
I agree that making the magic black box is very hard. Automatic model
selection, at scale, requires a lot of processing power.

My take is that Azure "just" makes it easier/quicker to run ML experiments,
and to deploy models. It's not entirely black box since you have to pick an
algorithm and parameters. I expect that once you've run your experiments and
found what works best, you should be able to get similar results with an open
source implementation of your chosen algorithm. But then you'd still have to
deploy your model somewhere — maybe using a platform like yhathq.com which
makes things more transparent?

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crt000
Of course Azure is a __much__ more complex service, but I am currently working
in a _somehow_ similar project:

[https://www.datapal.io](https://www.datapal.io)

The main goal of this project is to make it easy for people without knowledge
in predictive analytics to use their stored data in order to make predictions.

__It is still a very early prototype__, therefore the "predictive power" is
not great yet, and all kind of bugs are expected.

I have a lot of ideas on how to improve the service and your feedback would be
really appreciated in order to prioritize the next steps!

Thanks!

~~~
louisdorard
Sounds very exciting! This space is getting a bit crowded though (see
[http://www.quora.com/Who-are-the-main-competitors-to-the-
Goo...](http://www.quora.com/Who-are-the-main-competitors-to-the-Google-
Prediction-API/)). How would you differentiate Datapal from competitors such
as BigML, Predictobot, etc.?

~~~
crt000
Hey, thanks for your interest! Well, the market is very big and I think so far
all the competitors use different approaches. DataPal will soon include a
number of features that will make it more different. Stay tuned! ;)

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Killah911
I think saw a couple of differentiators from Google's prediction engine. Would
someone happen to have a more comprehensive view of how this is different from
Google Prediction API?

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louisdorard
The main difference between Google Prediction and Azure ML is that the former
doesn't require any knowledge of machine learning algorithms, whereas the
latter does. Google Prediction automatically selects the best algorithm based
on the data you uploaded. In Azure ML, you have to choose an algorithm (and
its parameters) yourself.

Other differences are that Azure also has a data transformation component, a
built-in text analysis tool, it can perform clustering tasks, and it makes it
easier to expose your trained models as APIs.

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felixrieseberg
If any YC startups are interested in checking out Azure, note that Microsoft
grants $60k in usage credits (as well as architecture and engineering
support). Shoot me a mail at felix.rieseberg@microsoft.com and I'll help you
out!

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nickbarnwell
Hey Felix, any chance that that offer is open to researchers as well? One of
the groups I'm involved with at UW needs a decent amount of compute power and
would love to move some stuff off our homegrown cluster of spare hardware.

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alceufc
Maybe this link can be useful for you:

[http://research.microsoft.com/en-
us/projects/azure/](http://research.microsoft.com/en-us/projects/azure/)

~~~
felixrieseberg
That's exactly the right approach!

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wfjackson
Some real world examples here.

[http://blogs.technet.com/b/machinelearning/archive/2014/07/1...](http://blogs.technet.com/b/machinelearning/archive/2014/07/14/how-
azure-ml-partners-are-innovating-for-their-customers.aspx)

There's also the Azure Machine learning university program with videos etc.,
but looks like it's for partners only, perhaps will be available to all once
it's out of preview.

[https://readytogo.microsoft.com/global/_layouts/RTG/Campaign...](https://readytogo.microsoft.com/global/_layouts/RTG/CampaignViewer.aspx?CampaignUrl=https://readytogo.microsoft.com/global/campaign/pages/machine%20learning%20university%20\(global\).aspx&WT.mc_id=Blog_MachLearn_General_DI)

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scruffydan
You can access the videos here:

[https://readytogo.microsoft.com/global/_layouts/RTG/Download...](https://readytogo.microsoft.com/global/_layouts/RTG/DownloadCampaign.aspx?campurl=https%3A//readytogo.microsoft.com/global/campaign/pages/machine%20learning%20university%20%28global%29.aspx)

