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Effective Amazon Machine Learning by Alexis Perrier

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Getting started and setting up

Creating a performing predictive model from raw data requires many trials and errors, much back and forth. Creating new features, cleaning up data, and trying out new parameters for the model are needed to ensure the robustness of the model. There is a constant back and forth between the data, the models, and the evaluations. Scripting this workflow either via the AWS CLI or with the Boto3 Python library, will give us the ability to speed up the create, test, select loop.

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