More importantly, any valid ML analysis requires that the source data is good, which is impossible to guarantee with this service.
Let's say you've got 2 steps: Prep the data, build the algo + infrastructure. Let's say for the sake of argument that they're about equal in terms of difficulty. If you can drastically reduce the amount of time the second takes, that will still be a valuable speedup. And I'll bet there's tools that could be included in these types of solutions that can help with data prep also (it doesn't have to be one or the other)
Databases for instance -- there's lots of options, some proprietary, some open source -- but you'd rarely consider building your own unless as an intellectual exercise. At what point does this become true for machine learning? Saying it doesn't do 100% of my job for me doesn't mean that it won't work as a product category.
Of course, this is coming from someone with a vested interest in the category, so take that with the requisite bias...
I've found most people who need machine learning usually want control of it/hosting it in house.
We'll also deploy to an HTTP endpoint and you can call it and get JSON response.
Does that address your concerns?
I know you guys are only targeting the very simple needs of people which is great. Keep in mind I'm far from your target audience being almost a little too deep in the machine learning side. Best of luck with it!
What value add would you want to see?
I've found that certain things, especially NLP pipelines, are usually very tricky to get right.
Let me give you one example from my day to day:
Sentence segmentation/tokenization ---> part of speech tagger ---> filter words by part of speech and then rank by tfidf scores for topic relevance scoring.
Computer vision also has its own problems ---> binarize images for detection of certain kinds of features vs needing colors, so different kinds of image transformations for handling of different kinds of object recognition, or scene detection.
I also do churn prediction, by the time you're done vectorizing users, why should I take time out of my day to then upload all of this to an external service when I could just run logistic regression, random forest or what have you locally? I typically calculate profit curves and the like as well.
Re: your word vectors and deep learning. I do this in my distributed deep learning lib I hand wrote myself[1]. In my word2vec computations, I have found it takes a significant amount of data to tune right, I usually want control over this pipeline as well.
Again, not your target audience ;).
To give you some helpful feed back, don't try being everything to everybody, own a certain niche really well and run with it. If you'd like to take this discussion offline, I'd be more than happy to help, email is in my profile. Again, good luck with the service!
Still, we've constantly been surprised by how well we can learn on raw CSV dumps from SQL joins or just log data.
Furthermore, small business owners or non technical folks can still understand the concept of "teaching" an algorithm: things like including demographic information increase predictive power etc.