From my book, Amazon Machine Learning: An Introduction:
Amazon Machine Learning, or AML, provides you access to widely applicable machine learning algorithms without having to run any servers. This type of learning is useful for making predictions based on a set of data for which answers are known. AML supports supervised learning with the stochastic gradient descent algorithm. The end goal of AML is to create a model, which is what will allow you to make further predictions based on past data.
AML supports three different kinds of predictions. For binary outcomes, where observations lead to a yes/no result, AML supports binary classification. An example would be whether or not a prospect is likely to sign up for a new account, given their past interactions with your company. For multi valued results, where observations lead to one of N results, AML supports multi class classification. A good example of this would be which product to show a customer, given what they’ve looked at and bought in the past. And, for numeric values, AML supports regression. An example of that would be predicting house prices based on sales data and house attributes.
If you are not trying to use existing data and create predictions out of it using supervised learning, but are trying to instead recognize images or tease out patterns in text, you may want to consider alternatives to AML.