Azure – Machine Learning as a Service(azure.microsoft.com)
azure.microsoft.com
Azure – Machine Learning as a Service
http://azure.microsoft.com/en-us/campaigns/machine-learning/
11 comments
The video is full of stock footage of business people doing businessy things but light on actual information, which is a shame because the service looks very interesting.
Our company mulled the idea of doing a similar product for years. Essentially putting a pretty UI on top of machine learning algorithms under the hood where you automatically determine which algo gives the best fit.
Ultimately shelved it because we couldn't find a good customer fit. For corporate enterprise users, our usual bread & butter, this has to be super secure so the idea of uploading gigs of proprietary company data or customer data rings alarm bells. Plus the data has to be cleaned up and ready to go. It just seemed like a lot of leg work before you even got to the payoff - and even then, having to explain why the predictive results weren't 100% accurate etc.
Curious to see how this plays out. I wonder if they're using vowpal-wabbit under the hood - I believe the guy behind that worked / works at MSFT.
Ultimately shelved it because we couldn't find a good customer fit. For corporate enterprise users, our usual bread & butter, this has to be super secure so the idea of uploading gigs of proprietary company data or customer data rings alarm bells. Plus the data has to be cleaned up and ready to go. It just seemed like a lot of leg work before you even got to the payoff - and even then, having to explain why the predictive results weren't 100% accurate etc.
Curious to see how this plays out. I wonder if they're using vowpal-wabbit under the hood - I believe the guy behind that worked / works at MSFT.
I'm on the opposite end of the market, but I'd be curious to see who would use ML as a service. I've never seen the use case for it being in the ML space myself. Anyone care to step up and state their use case?
How much data do you typically throw at these APIs? How effective have they been for you overall? Did you need a super accurate model?
How much data do you typically throw at these APIs? How effective have they been for you overall? Did you need a super accurate model?
Like most other exponential growth patterns, there is suddenly HUGE number of people now wanting to use ML but don't have necessary background. Pretty much every other enterprisy developer who had been happily spending their lives pushing data from RDBMS to/from UI now wants to try out ML for something or other. Unfortunately current state of ML framework is a minefield. If you don't know your precision from recall or confusion table really confuses you or have no clue when you have been merely overfitting all along, you are screwed.
I can see huge market if you can bottle down complexities of ML in a nice easy to use package. A very easy to use ML service or tool would allows you to import your data with few clicks, apply normalization such as stemming or edit distance by pick-n-choose without writing any code, has nice generic featurization library, can run tons of algorithms with large number of parameter sweeps, does automatic feature selection, takes care of maintaining test and validation sets etc. This kind of thingy would be super popular. My guess is that it has to run in cloud because it eliminates all the setup and updates plus doing what I described even on moderate size data sets usually takes hours on single machine. Now throw in plug-n-pray deep learning algos which even few ML experts are familiar with and can require significant GPU based infrastructure.
Can this be reality? In my opinion, this is more in line of those graphical tools that claims you can create programs without having to learn programming. It's impressive when you do demo but don't last long in battle grounds. Ultimately, if you want to write program, you will need to get your hands and cloths dirty with oil stinks. If you want to do machine learning, you will need to start from stats and probability.
I can see huge market if you can bottle down complexities of ML in a nice easy to use package. A very easy to use ML service or tool would allows you to import your data with few clicks, apply normalization such as stemming or edit distance by pick-n-choose without writing any code, has nice generic featurization library, can run tons of algorithms with large number of parameter sweeps, does automatic feature selection, takes care of maintaining test and validation sets etc. This kind of thingy would be super popular. My guess is that it has to run in cloud because it eliminates all the setup and updates plus doing what I described even on moderate size data sets usually takes hours on single machine. Now throw in plug-n-pray deep learning algos which even few ML experts are familiar with and can require significant GPU based infrastructure.
Can this be reality? In my opinion, this is more in line of those graphical tools that claims you can create programs without having to learn programming. It's impressive when you do demo but don't last long in battle grounds. Ultimately, if you want to write program, you will need to get your hands and cloths dirty with oil stinks. If you want to do machine learning, you will need to start from stats and probability.
When I said I'm on the other side, I met I'm one of those crazy machine learning guys[1] who doesn't see a business model in the full blown package.
You're right that this would be hard, but I'm wondering if the cost benefit analysis is there for it. I looked at this approach, and I don't believe it is relative to the costs incurred for something like this. I'd be willing to give it a shot at some point maybe, but neural nets behind the scenes for me involves more fun data behind fire walls ;).
[1] http://wired.com/2014/06/skymind-deep-learning/
You're right that this would be hard, but I'm wondering if the cost benefit analysis is there for it. I looked at this approach, and I don't believe it is relative to the costs incurred for something like this. I'd be willing to give it a shot at some point maybe, but neural nets behind the scenes for me involves more fun data behind fire walls ;).
[1] http://wired.com/2014/06/skymind-deep-learning/
> "this is more in line of those graphical tools that claims you can create programs without having to learn programming"
There's a comparison to be made with graphical tools that let you create websites without knowing html/css/javascript, like squarespace.com for instance. It's enough to cover people's needs in 80% of the situations. See this article by Scott Brave for more: http://gigaom.com/2012/12/22/we-dont-need-more-data-scientis... .
There's a comparison to be made with graphical tools that let you create websites without knowing html/css/javascript, like squarespace.com for instance. It's enough to cover people's needs in 80% of the situations. See this article by Scott Brave for more: http://gigaom.com/2012/12/22/we-dont-need-more-data-scientis... .
I work for a fulfillment house, we do tens of thousands of orders a month. Despite this, we're still what would be considered a "small business". Forecasting has been a really big pain point so far so we're watching AzureML closely. If they can make this as low-configuration as they promise I think it'll be killer for us, along with many other small businesses with big logistics components that have a LOT of data they wish they could act on.
I appreciate the feedback, it's good to see real business for a service like this. I'll keep an eye on this space. If it's interesting maybe I'll jump in.
Have you tried using BigML? (which to me is extremely similar to what Azure ML is pitching)
The privacy concerns with throwing loads of data at a third party api is staggering and perhaps the main driver for doing ML in-house.
This is why I do on premise deployments for my ML stuff. A lot of money is being left on the table with SAAS in this space I think. I'm of the opinion models should come to the data not the other way around.
It's not really an important feature--if it was, people wouldn't have been gushing their details into massive databases over the past decade.
It's nice to see a set of tools that lets even the most simple of business people exploit their customers as effectively as, say, Facebook or Google.
It's nice to see a set of tools that lets even the most simple of business people exploit their customers as effectively as, say, Facebook or Google.
Anyone know how this is different from, say, prediction.io?
http://prediction.io/
http://prediction.io/
Prediction.io needs a server to run on, and it focuses on recommendation problems, so it doesn't seem to do classification and regression for instance.
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Looks really interesting. Anyone find any links with actual info, as this page is pure fluff!?
This has the potential to be huge, if they decide to tap into the vast knowledge-base that Microsoft Research has developed over the years. I would jump at the chance to use an original implementation of their research on Decision Forests.
Wow - Google Prediction like API with CIO approved corporate drones? Check! Only took them 4 years to catch up.
The "ML Studio" that briefly appears in the video looks like a slicker version of Weka.
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Missed a trick to call it Mlaases.