We are no longer updating the Amazon Machine Learning service or accepting new users for it. This documentation is available for existing users, but we are no longer updating it. For more information, see What is Amazon Machine Learning.
When to Use Machine Learning
It is important to remember that ML is not a solution for every type of problem. There are certain cases where robust solutions can be developed without using ML techniques. For example, you don’t need ML if you can determine a target value by using simple rules, computations, or predetermined steps that can be programmed without needing any data-driven learning.
Use machine learning for the following situations:
You cannot code the rules: Many human tasks (such as recognizing whether an email is spam or not spam) cannot be adequately solved using a simple (deterministic), rule-based solution. A large number of factors could influence the answer. When rules depend on too many factors and many of these rules overlap or need to be tuned very finely, it soon becomes difficult for a human to accurately code the rules. You can use ML to effectively solve this problem.
You cannot scale: You might be able to manually recognize a few hundred emails and decide whether they are spam or not. However, this task becomes tedious for millions of emails. ML solutions are effective at handling large-scale problems.
