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.
For a model to predict accurately, the data that it is making predictions on must have a similar distribution as the data on which the model was trained. Because data distributions can be expected to drift over time, deploying a model is not a one-time exercise but rather a continuous process. It is a good practice to continuously monitor the incoming data and retrain your model on newer data if you find that the data distribution has deviated significantly from the original training data distribution. If monitoring data to detect a change in the data distribution has a high overhead, then a simpler strategy is to train the model periodically, for example, daily, weekly, or monthly. In order to retrain models in Amazon ML, you would need to create a new model based on your new training data.
