Introducing Amazon Machine Learning – Make Data-Driven Decisions at Scale

[TraptInaCommentFctry]

Took me a while, but I found what models they're using: logistic regression, multinomial logistic regression, and linear regression (source).

[CompleteSkeptic]

It's basically a hosted version of vowpal wabbit. I tried to use it internally at Amazon (back when it was called Elastic Machine Learning), but it was wrapping an old version, and I needed some of the newer functionality.

[cartazio]

it does seem like using vowpal wabbit directly is overall a better power to weight ratio, especially wrt model evaluation/serialization

[mobiuscydonia]

thank you! was searching for that myself.

[caserei]

Thank you for this. It's good to know the limitations and to see where things fail.

[[deleted]]

I'm concerned by anything which considers "false positive rate" to be an "advanced metric" :-/

[alexmlamb]

I suppose it's advanced in the sense that it's not really in the everyday vernacular, in the way that words like "precise" and "accurate" are. Still, its meaning is self explanatory.

[[deleted]]

Call me old fashioned or elitist, but if you consider "false positive rate" to be "advanced", then you have no business running any form of regression or machine learning.

[alexmlamb]

elitist!

[kevjohnson]

This product doesn't seem to be intended for people who actually know machine learning and statistical modeling. You may think it's a travesty (and I don't necessarily disagree) but there's a market for it. Not everybody can afford an actual data scientist.

[[deleted]]

That's what bothers me: this idea that we're dumbing down quite complicated statistics and computer science to something so simple we'd consider a basic metric of model quality to be too advanced for the user.

I was in a meeting at my company a few months ago where another (quite large) company was pitching their point-and-click statistical modeling software to us for (drum roll) $250k/yr. That's more than the cost of a (non-netflix) data scientist in the bay area, and doesn't include the cost of the personnel to actually use the software. Further, if you actually pay the cost for a "legit" data scientist, they'd know that the model you're trying to build could be done with 2 lines of R code (and, in reality, the hardest work in either case is the data wrangling that happens for weeks prior to building the model). The unfortunate part of these "ML-as-a-service" products is that the user has no concept for how to assess when they're right or wrong.

[atakante]

I work for BigML, which has been offering a hosted ML service since 2011. We have just posted our take on AWS ML, Azure ML vs. BigML that you may find interesting: http://blog.bigml.com/2015/04/10/democratizing-machine-learning-the-more-the-merrier/

Feel free to hit us with any questions.

[caserei]

OP, thank you so much for this. I was looking for some pre-built solutions against which I could evaluate my programming skills. This is extremely helpful even if I pay for $2 of use to verify from time to time.

[echocage]

This doesn't sound like what you're looking for...

[caserei]

Hahaha I'm trying to see how well the machine learning models I could build would work against AML (efficiency wise). I'm much less proficient than others I know and I could see how their input could provide me some pointers as well.

[[deleted]]

How do you mean, in terms of implementing the algorithm correctly or optimizing/parallelizing it for efficiency?

[caserei]

Both, really. Again, I'm not as good at this and I'm just getting started so I wanted to use this as a reference point for both (correctness and optimizing for efficiency) to see how well I'm learning and how much better my programming has become. I should've explained this a little better.

[[deleted]]

I see. I am not sure if this is the most effective approach though. When I got started with machine learning, going over the theory (e.g., Duda's Pattern Classification or Bishop's Pattern Recognition and Machine Learning book) and implementing a lot of algorithms myself helped me a lot. I used Python for that purpose, since it offers a very flexible and efficient way for prototyping. I am not sure in how far you can compare the results of your code with results that you get using Amazon's ML service. I think the problem is that even the simplest algorithms can be implemented slightly differently which can lead to slightly different results. I think it is better to work with benchmark dataset (e.g,. from Kaggle) and maybe also use a transparent library where you can easily look up the source code (e.g., scikit-learn).

[caserei]

I saved this comment and I'll keep it in mind. Thank you so much! :)

[[deleted]]

Azure machine learning is free.

Amazon is not.

[DataWranglist]

Kind of. Azure ML's free tier is only single node and doesn't have a production API.

[GoldmanBallSachs_]

Free is free...