41–50 of 98 posts
"Mark Cuban recently talked about it as the most important technology to ramp up on, to avoid becoming a “dinosaur”" I wonder if this impresses this blog's audience, or does exactly the opposite...
I want to know if any of the pictures from my trail camera include deer. It sounds like Rekognition might be the answer for this.
I would go with a DIY solution, since Rekognition will get expensive quick ($1/1000 images). If you did 1fps running continuously, you'd pay $87/day.
Nearly any ML framework with an ImageNet pre-trained model will work. Accuracy will be better if augmented with more labeled deer images (ImageNet probably only has frontal poses) but should be alright straight out of the box.
You could run the whole thing locally on a cheap android phone for free instead of AWS cloud pricing, 1fps will be fine with TensorFlow or MXNet on Android. Then just setup email alerts.
Has anyone used Rekognition? We're thinking about pumping traffic cam feeds into it in cities for vehicle counting but don't want to waste time if it's junk.
We have recently set up a TensorFlow assessment function in AWS lambda, and got very close to the maximum allowed size of a lambda function (250MB) with the trained model currently being 85MB, and the TensorFlow libraries and binaries taking up another 140 or so megabytes by default ( I feel like Amazon could do some work in this area to support users to use their own engines and not be bound to AWS AI Platforms and…
Quick slightly unrelated question: Does anyone have a comparison of using Google cloud services vs AWS for machine learning? I'm planning to pick one, and I was leaning towards Google Cloud Services because of the TensorFlow support and the fact that Google is big on ML, making it likely that it's something that Google will support and be good at. With this blog post, I'm not sure.
I guess it's more comparable with Google Cloud Functions: https://cloud.google.com/functions/
Quick slightly unrelated question: Does anyone have a comparison of using Google cloud services vs AWS for machine learning? I'm planning to pick one, and I was leaning towards Google Cloud Services because of the TensorFlow support and the fact that Google is big on ML, making it likely that it's something that Google will support and be good at. With this blog post, I'm not sure.
Both providers offer you raw VMs with GPUs and such so you can run popular machine learning frameworks yourself by hand. After that the three providers diverge a bit, and I've not seen a good writeup myself. Roughly:
- Google has both a hosted TensorFlow (Cloud ML) as well as specific, pre-trained models you can simply use (Cloud Vision, Cloud Speech, etc.). For an easy to use interface, we have direct TensorFlow (and more) integration in Datalab.
- AWS also has some pre-trained services (Rekognition, Polly, Lex) but for "obvious" reasons doesn't do hosted TensorFlow. Instead Amazon Machine Learning is a bit more like Azure's offering: "Put data in, wire up stuff in the console and hit go".
If you're really interested in ML, my biased opinion is that you'll be using TensorFlow. And as you surmised, we're committed to making TensorFlow the "best" ML framework and making sure it runs well on Google Cloud. Like Kubernetes, we're not going to handicap it elsewhere, but having it managed and accelerated for you, is extremely convenient.
[Edit for formatting. I also should have mentioned there will be lots of ML-related talks at NEXT in San Francisco in two weeks!].
I want to know if any of the pictures from my trail camera include deer. It sounds like Rekognition might be the answer for this.
We have recently set up a TensorFlow assessment function in AWS lambda, and got very close to the maximum allowed size of a lambda function (250MB) with the trained model currently being 85MB, and the TensorFlow libraries and binaries taking up another 140 or so megabytes by default ( I feel like Amazon could do some work in this area to support users to use their own engines and not be bound to AWS AI Platforms and…
I'm curious what sort of things you did to shrink your model?
Have you considered pulling the model data from S3 outside of the main lambda handler and seeing if that negatively impacts performance -- with a cloud watch event running every 5 minutes or so to keep the function warm?
I am a complete noob to the AI space but I was wondering whether the following is possible (in AWS). I have a million scanned images of court documents. Some are briefs, some are motions, some are court orders, etc... Given that I have images and their types, could I "train" the AI with these million documents to recognize a new image that might come in?
Is that possible with AI? Yes, almost trivially. Is that possible with AWS AI? > Finally, we provide AI engines, a collection of open-source, deep learning frameworks for academics and data scientists who want to build cutting edge, sophisticated intelligent systems, pre-installed configured on a convenient machine image. Which is to say anything that TensorFlow can do, AWS AI can do. So yes. But is it possible witho…