We provide a machine image with TF, MXNet and others pre-installed, along with Keras, CPU and NVIDIA divers, and other libraries for deep learning. We just added Ubuntu support too:
https://aws.amazon.com/blogs/ai/the-aws-deep-learning-ami-no...
http://www.allthingsdistributed.com/2016/11/mxnet-default-fr...
but to each his own!
Are there (artificial) datasets that can be used that showcase particular fit for deep learning?
I think there is way to little research in building artificial datasets (using domain knowledge of course).
It might even be possible to run these generative models and have this type of data very soon.
I agree though, the kind of artificial / play-against-yourself datasets that the folks at DeepMind created for say Alpha Go are an entirely different beast.
[1] https://cloud.google.com/blog/big-data/2017/02/google-cloud-...
[2] https://research.google.com/research-outreach.html#/research...
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!].