This is easily the most interesting announcement so far. Machine learning has so many applications, but its use is constrained by the high barriers to entry. Recommendation engines, for example, are huge sales drivers, but few among even the largest ecommerce stores use them. A simple prediction interface that's built on the ML expertise at Google is a win for everyone.
For very specific problems, yes. The translation classification example is definitely a type of prediction problem that would be good for this service.
However, this is far from a silver bullet to ML problems. It can be quite dangerous, for example, to send off a bunch of data to google and immediately trust their analysis without knowing the underlying application of their algorithm. As a researcher in this area, what I would LOVE is if I could create my own algorithm, send it to Google, upload enormous amounts of data, and get back a result. Because right now it's difficult to scale complex algorithms to datasets in the GB-TB-PB range. Mahout is taking a valid stab at this problem though.
I don't think Google has some magic "make this algorithm scale to huge datasets" box that they can run things through. The reason why Google can run predictions on huge datasets is because they designed an algorithm that has lower accuracy but greater scalability; this service seems to be just them giving you an API to that algorithm. I think your comment sounds like "I like your algorithm, but I'd rather use my algorithm, so please run my algorithm but make it have the features of your algorithm"...
No, I don't mind using their algorithms (I'm sure they are correct anyways). I just mind that I can't tinker the algorithm to be optimized for my special use case scenario.
You can have accuracy and scalability. It's just that normally you run these types of things in memory on one machine, not on tens to hundreds of thousands of machines. So these algorithms have to be decoupled and that isn't easily done (but I'm sure Google figured it out a long time ago).
For most people though, I would think a service like this could easily prove to be good enough. What would you suggest that would be a significant performance or productivity improvement over cloud computing?
If I have a graph of customers and what products they've bought, Directed Edge makes it easy to figure out what products to recommend to each customer. Generating the same recommendations with Google's prediction API is not as obvious to me. What data do you train it with? The nth product the customer bought as the output, and the 1..n-1th products as the input?
Maybe I'm slow, but I don't see how google's prediction API is a good replacement for collaborative filtering type recommendation engines.
The standard supervised-learning stuff doesn't have that high a barrier to entry these days, although offloading it as a service to the cloud is of course even easier. But on the client side, things like Weka are simple enough that plenty of CS undergrads use them in intro data-mining/AI/stats classes: http://www.cs.waikato.ac.nz/ml/weka/
Off-the-shelf recommendation systems are a bit trickier though, yeah.
The API seems pretty simplistic. Does it choose the different supervised learning algorithm underneath without input from the user or does it just use one supervised learning algorithm? Their website does not give any algorithmic details.
