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Interestingly, Microsoft's ToS:

You grant to Microsoft and will secure and maintain all rights in the Data necessary for us to: ... (iv) improve the Service Components and other Microsoft products and services

http://research.microsoft.com/en-us/um/legal/CognitiveServic...

Google Vision API:

5.2 Use of Customer Data. Google will not access or use Customer Data, except as necessary to provide the Services to Customer.

https://cloud.google.com/terms/

Do you think that improving the model does not fall under "provide the Services to Customer?"

IANAL. Does it?

> In other words, why would anyone invest in ML as a service with so many potential forces acting against your continued success?

a) You see little chance of remaining relevant in your field without biting the bullet & providing training data as part of the package deal

b) You see "product/service takes off" as a far off dream, with many near-term wins that will make the tradeoff worth it for you. For now. And that has to be good enough.

c) You wonder aloud what kind of data that might be, that could be combined into something else, along with your data. Your best strategy people tell you it's not worth getting bothered over, given the nature of your project. Now is the time to take risks.

> In other words, why would anyone invest in ML as a service with so many potential forces acting against your continued success?

Because those "forces" are wayyyy out there compared to the bigger risk, namely "not having any better ideas"

Nobody said MS has to market this to companies that see MS as a potential competitor.

I think this comes down to making sure your advantage is inhouse but outsource the other stuff.

For the vast amount of companies where ML isn't in any way core, this is great. For example we've got a little internal app that includes a profile picture.

Currently we pre-crop photos before uploading. Obviously not the best solution. Putting together a little javascript thingy to allow the user to crop is somewhere deep in the backlog. Now this exists I'm very tempted to implement a little feature to auto crop around the face on upload. Nothing there to steal, definitely not worth rolling our own ML system to do it but a very nice thing to now be available in our toolkit.

You should check out imgix as well. They have a really nice crop feature along with automatic quality adjustment and serving different formats (webp/jpg) where available. https://docs.imgix.com/apis/url/size/crop

Yeah I think this is a good use case. But is that going to be a huge business for Microsoft/Google/Amazon?

There are serious companies (like Netflix) that put their entire infrastructure on cloud platforms. If the concerns the OP raises are valid, no company for whom ML is a big (read: high usage, high paying) feature is going to rely on these cloud services. And startups that do rely on the cloud services for their MVPs are going to move off as quickly as they can. I can't think of any successful cloud platform for which MVPs and small features are the main customer use case. Heroku comes to mind, but even then there are mid-size companies that do rely mostly on Heroku.

Yes, this is what I am trying to say. It looks as if the use-cases, if not the market-size itself, is a lot less ambitious if you choose to use these services.

But also - what happens when you wish to expand and grow? My guess is, if this capability was developed in house, you would almost certainly find it easier to not just scale without incurring a "success tax", but most importantly you are now exposed to the deep bowels of the ML process which should certainly give you far greater insight into potential tangentially related services.

An example: you use a natural language processing API and get all the brand mentions in some text the customer has submitted - certainly a useful service for competitive research. As your company scales, now you are more or less beholden to the ML service - if nothing else at least for the consistency of results, which is likely to make you super cautious about rolling your own. But you don't know how the results (brand mentions) were actually inferred. If you were to ask, "Hmm.. what other types of entities can I extract and have it be more useful for our customers? What about only getting brand mentions if it is in the same paragraph as a complaint?" - you might face some options which you might not really like - for one, the way the ML API is structured might actually make this operation expensive (after all, the data is pretty adjacent before being sent to the ML API, but not necessarily so once it is processed). Also, since you never understood how the brand mentions were extracted, it is very likely you are missing out on a ton of insight you could gain by rolling out your own capabilities.

And not to mention: if the ML capability has been outsourced, and if ML is indeed going to drastically impact your industry, then you would certainly not want to be in the position where your competitor can easily disrupt you merely because they spent the time to develop such capabilities in house.

What your saying is certainly true for some areas, but I feel like there is a big market for commodity ML. I don't worry that Microsoft developed our database and might therefore steal our business from under us. I just need a nice relational datastore that "just works". The database is absolutely critical to our application working, but it's not in any way what differentiates us. For many people, ML services will be the same sort of thing.

Face recognition is built into OpenCV. It should be very easy to implement this feature yourself without relying on any third party.

http://docs.opencv.org/2.4/modules/contrib/doc/facerec/tutor...

Pre-processing and alignment are going to be way off. Even if it wasn't, then you have (for face recognition itself) eigenfaces or fisherfaces (bad) or maybe LBP(maybe barely acceptable). Your performance is going to be abysmal compared to serious face recognition technology.

My experience is that OpenCV face detection works relatively well if you are facing the camera directly. We used an opensource face rec built on top of it, and it was okay to classify between a small number of known faces.

I saw some demos of M$FT's apis at a recent tech event and it wasn't obvious that the results with their system were significantly better. The biggest hole seemed to be that camera frames were being sent to the cloud, and there were issues with the presenter's quota (forget the exact problem).

But overall, I think the Cognitive Services are a step in the right direction. They generally make sense for businesses who don't have a full-time team of ML/CV experts.

opencv is not the easiest thing to add to your deployment

Put it on fiver

There are a ton of reasons.

For example, it could just be that ML is not your business. If you're a image hosting platform, you don't necessarily want to build your own NSFW image checker. There's no harm in leveraging something else out there that works great, assuming that you can bankroll it.

Alternatively it can be used as a stop-gap. You're in the middle of implementing your own ML system (or planning to), but you can use a 3rd party to fill the gap until you're ready to go live with your own version.

This is also a great way to test new features and see if users respond to it.

In my opinion it boils down to if either you need to own the model, or guarantee the quality of it, then perhaps you should consider owning it yourself. Otherwise, it's fine to consider using a third party.

Yeah, the main reason, assuming you're a "ML-based business", is you want to validate your market and not spend a ton of money creating something that's not profitable.

Anyway, there are many more obvious use cases. Other apps/games/services that need to detect faces or filter out porn and so on. Imagine a dating site that estimates age and more based on photos, while also filtering out NSFW images.

Because the standard enterprise IT department is filled with people barely capable of keeping up a website and a database.

I've been building a search/recommendation site for lectures, and these AI services have been great for prototyping. (https://findlectures.com)

While I am just treating this as a research project, some of the content is from Youtube, so they are effectively a competitor. Google has been using their speech recognition to help the Youtube search (which has helped me), but they so much data that it's hard for them to solve niche use cases.

But in those APIs there is no feedback loop to tell MS/Google "hey you classified this wrong: here's what it actually is" (example)

The kind of startup that doesnt want to run out of startup capital getting strung up managing software and infrastructure but would rather get to market and then concern themselves with the trivial concerns you raised.

One could apply the same argument to cloud hosting no? You are paying Amazon and Google to give them data on your storage, traffic, and processing needs.

I've always wondered if there is anything stopping this data from being used to inform market opportunities for Amazon, Google or Microsoft.

It depends whether the rented ML is key to your product / startup. Alternative is running your own mini service with API based on open source software and public models or slightly modified models. Look at digits, deepdetect, bit fusion, etc..

Presumably because they've got algos so awesome that you can't replicate the performance and that their training sets (by virtue of being a public service) are much larger than you'd be able to obtain yourself.

I share your scepticism though.