Machine Learning in the Cloud, with TensorFlow

Access to this API (and docs) is currently invite-only. Sign up for the limited preview: https://services.google.com/fb/forms/machinelearningpreview/

You should see the (pile of numbers) project id when you visit https://console.cloud.google.com in the upper left inset. Alternatively, at any place in the console you can hit the Settings gear and select Project Info (which should have both your numeric project number as well as your alphanumeric project identifier).

Disclosure: I work on Compute Engine.

Yes. Have your advisor email me (dga at cs . cmu . edu) and I'll send him / her pointers to some of the programs Google has for faculty. Note: cloudml alpha is a limited invite-only thing, so is unlikely to work for you on a short timescale, but nothing stops you from using GCE as a source of cycles on which to run your own install of TF in the meantime.

At the risk of giving a serious answer when your phrasing makes it seem like you're trolling: No. The Cloud ML service is your data and your model and your training results (except as required for google computers to touch it to train it and store it - but the results of that training are yours.)

The same question was asked earlier of the Vision API, with the same answer: https://news.ycombinator.com/item?id=11128444

At a high level, you can think of it as having mostly the same properties as if you rented a bunch of GCE machines (or AWS machines) and ran TensorFlow on them, with your data stored in GCS (or S3 or whatever). The difference is that Cloud ML handles the scaling pain for you -- managing the machines, starting and keeping tasks running, load balancing, etc.

(disclaimer: I'm working on TensorFlow, not Cloud ML.)