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](https://docs.aws.amazon.com/machine-learning/latest/dg/what-is-amazon-machine-learning.html).

# Using the Model to Make Predictions


Now that you have an ML model that performs well, you will use it to make predictions. In Amazon Machine Learning, there are two ways to use a model to make predictions:

# Batch Predictions


Batch prediction is useful when you want to generate predictions for a set of observations all at once, and then take action on a certain percentage or number of the observations. Typically, you do not have a low latency requirement for such an application. For example, when you want to decide which customers to target as part of an advertisement campaign for a product, you will get prediction scores for all customers, sort your model’s predictions to identify which customers are most likely to purchase, and then target maybe the top 5% customers that are most likely to purchase.

# Online Predictions


Online prediction scenarios are for cases when you want to generate predictions on a one-by-one basis for each example independent of the other examples, in a low-latency environment. For example, you could use predictions to make immediate decisions about whether a particular transaction is likely to be a fraudulent transaction.