5 ms·

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

by PJDK 10y ago

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.

coldsmoke 10y ago
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 https://docs.imgix.com/apis/url/size/crop
argonaut 10y ago
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.
- thr0waway1239 10y ago
  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.
  - PJDK 10y ago
    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.
adrianN 10y ago
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/tutorial/facerec_video_recognition.html http://docs.opencv.org/2.4/modules/contrib/doc/facerec/tutor...
- alvarosm 10y ago
  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.
  - trapperkeeper79 10y ago
    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.
- rcpt 10y ago
  opencv is not the easiest thing to add to your deployment
ryuker16 10y ago
Put it on fiver

by PJDK 10y ago

coldsmoke 10y ago

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 https://docs.imgix.com/apis/url/size/crop

argonaut 10y ago

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.

thr0waway1239 10y ago
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.
- PJDK 10y ago
  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.

adrianN 10y ago

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

alvarosm 10y ago
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.
- trapperkeeper79 10y ago
  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.
rcpt 10y ago
opencv is not the easiest thing to add to your deployment

ryuker16 10y ago

Put it on fiver