Using the AWS web interface to manage and run your projects is time-consuming. In this chapter, we move away from the web interface and start running our projects via the command line with the AWS Command Line Interface (AWS CLI) and the Python SDK with the Boto3 library.
The first step will be to drive a whole project via the AWS CLI, uploading files to S3, creating datasources, models, evaluations, and predictions. As you will see, scripting will greatly facilitate using Amazon ML. We will use these new abilities to expand our Data Science powers by carrying out cross-validation and feature selection.
So far we have split our original dataset into three data chunks: training, validation, and testing. However, we have seen that the model selection can be strongly dependent on the data split. Shuffle the data — a different model might come as being the best one. Cross-validation is a technique that reduces this dependency by averaging the model performance on...