Question: Why do I have to implement hyperparameter selection?
For me, the promise of in-the-cloud machine learning is that I can call 'train' method, and specify one single hyperparameter: training budget (i.e. $). Perhaps also the max time before I am returned a trained model.
This is exactly what we're enabling with our ML Platform (currently in private beta). Such a system needs to be built on top of fast & scalable ML technology with smart & efficient tuning/optimization.
Would love to hear about your use cases & get you on the beta.
There are different ways of finding optimal hyperparameters, and while a cloud system might be capable to provide a mechanism that can figure this out on its own, generally this will be less efficient...
For me, the promise of in-the-cloud machine learning is that I can call 'train' method, and specify one single hyperparameter: training budget (i.e. $). Perhaps also the max time before I am returned a trained model.
That's it. Can you do that?