Amazon Machine Learning models rebuilding possibilities

Question

There is only 2 kinds of in-built prediction/classification models in AWS Machine Learning. Logistic regression and linear regression. Is it possible somehow in current version of AWS ML to:

1) Re-build this what is under the hood of logistic and linear regression models

2) Build your own models written in Python/R, implement them on AWS ML and run things such as neural nets, random forests, clustering alghoritms?

In AWS ML Developer Guide latest version I could not find answers on those questions explicite, that it is impossible to do so. Any tips?

Answer 1

A bit of background first...

Amazon Machine Learning can build models for three kinds of machine learning problems (binary/multiclass classification & regression). As you previously mentioned, the model selected and trained by the platform is abstracted from the user.

This "black box" implementation is perhaps the largest deficiency of Amazon's machine learning platform. You have no information on what model or how the model is trained (beyond, for ex. linear regression, stochastic gradient descent). Amazon is quite clear that this is intentional, as they want the platform to be built into an application, and not just used to train models for one. See the 47:25 and 53:30 mark of this Q&A.

So, to answer your questions:

1. You cannot see how the exactly models have been trained, for example what constants in a linear regression (although you may be able to deduce by testing the model). When you query the model, the response includes a field which indicates the algorithm used for that particular model (for ex. SGD). A full list of learning algorithms can be found here.
2. Unfortunately not. You cannot create your own models and import them into AWS Machine Learning, meaning that no decision trees or neural network models can run on the platform.