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?
Comments
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?